{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48</td>\n",
       "      <td>189</td>\n",
       "      <td>950200</td>\n",
       "      <td>48189950200</td>\n",
       "      <td>9502</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>6306913</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>POLYGON ((-101.73553 34.21051, -101.73553 34.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>219</td>\n",
       "      <td>950400</td>\n",
       "      <td>48219950400</td>\n",
       "      <td>9504</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>12691656</td>\n",
       "      <td>5302</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-102.41616 33.56994, -102.41616 33.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48</td>\n",
       "      <td>219</td>\n",
       "      <td>950300</td>\n",
       "      <td>48219950300</td>\n",
       "      <td>9503</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>12186639</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-102.36763 33.58398, -102.36763 33.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>219</td>\n",
       "      <td>950100</td>\n",
       "      <td>48219950100</td>\n",
       "      <td>9501</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>214157569</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-102.28192 33.72410, -102.28190 33.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48</td>\n",
       "      <td>219</td>\n",
       "      <td>950600</td>\n",
       "      <td>48219950600</td>\n",
       "      <td>9506</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>358638163</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-102.31826 33.41007, -102.31598 33.4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20 NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        48        189    950200  48189950200   9502  Census Tract   G5020   \n",
       "1        48        219    950400  48219950400   9504  Census Tract   G5020   \n",
       "2        48        219    950300  48219950300   9503  Census Tract   G5020   \n",
       "3        48        219    950100  48219950100   9501  Census Tract   G5020   \n",
       "4        48        219    950600  48219950600   9506  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20    ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S    6306913         0  ...       84       84        0        0   \n",
       "1          S   12691656      5302  ...       57        0        0       57   \n",
       "2          S   12186639         0  ...       51        0        0       51   \n",
       "3          S  214157569         0  ...        0        0        0        0   \n",
       "4          S  358638163         0  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        6        0        0        6   \n",
       "1        0        0        0        0        0   \n",
       "2        0        0        0        0        0   \n",
       "3        0        0        0        0        0   \n",
       "4        0        0        0        0        0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-101.73553 34.21051, -101.73553 34.2...  \n",
       "1  POLYGON ((-102.41616 33.56994, -102.41616 33.5...  \n",
       "2  POLYGON ((-102.36763 33.58398, -102.36763 33.5...  \n",
       "3  POLYGON ((-102.28192 33.72410, -102.28190 33.7...  \n",
       "4  POLYGON ((-102.31826 33.41007, -102.31598 33.4...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "#tractGeomFile = gpd.read_file(\"state_map_files/fl_pl2020_t.shp\")  #Boo!  Only has geometries.  do not use\n",
    "tractPopFile = gpd.read_file(\"state_map_files/tx_pl2020_t.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a7eb6d2e-203e-42f9-9544-f86ed00b6dcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.type == simplePoly.type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 6896 popn tracts for TX\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"TX\"\n",
    "tractGeom = tractPopFile['geometry'] \n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population TX voter-age population\n",
      "state pop= 29145505 VAP pct Hispanic is  0.3616146469288919\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP TX\n",
      "total state pop= 29145505 , VAP pct Black is  0.12016966437551162\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for TX\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for TX\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n"
     ]
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d34b5fe5-6f02-413a-8c7b-c6662fa756ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#convex-hull these right away  (decision for each state - YES for FL)\n",
    "for m in range(nTracts):\n",
    "    if notPoly[m] == 1:\n",
    "        for geom in tractGeom[m].geoms :\n",
    "            xg,yg = geom.exterior.xy\n",
    "            plt.plot(xg,yg)\n",
    "        tractGeom[m] = tractGeom[m].convex_hull\n",
    "        x,y = tractGeom[m].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "        notPoly[m] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a05c0ec2-d208-45b8-8ff6-c1f1f583ad5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For TX, we do not bother removing low-population Gulf tracts from map.  They are thin.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b0e9e246-a558-475c-8181-075a9421034e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6896 0\n"
     ]
    }
   ],
   "source": [
    "print(len(isSkippedTract),np.sum(isSkippedTract))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d63968a2-afc1-4913-863c-4c415fb9c0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "isSkippedTract = [0]*nTracts\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CNTY</th>\n",
       "      <th>COLOR</th>\n",
       "      <th>PREC</th>\n",
       "      <th>PCTKEY</th>\n",
       "      <th>CNTYKEY</th>\n",
       "      <th>G20VR</th>\n",
       "      <th>G20SSVR</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>...</th>\n",
       "      <th>G20SSCRBUS</th>\n",
       "      <th>G20SSCDTRI</th>\n",
       "      <th>G20SSCLOXF</th>\n",
       "      <th>G20SCCRRIC</th>\n",
       "      <th>G20SCCDFRI</th>\n",
       "      <th>G20SCCRYEA</th>\n",
       "      <th>G20SCCDCLI</th>\n",
       "      <th>G20SCCRNEW</th>\n",
       "      <th>G20SCCDBIR</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>113</td>\n",
       "      <td>7</td>\n",
       "      <td>1104</td>\n",
       "      <td>1131104</td>\n",
       "      <td>57</td>\n",
       "      <td>2745</td>\n",
       "      <td>39.5</td>\n",
       "      <td>221</td>\n",
       "      <td>1173</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>195</td>\n",
       "      <td>1157</td>\n",
       "      <td>32</td>\n",
       "      <td>216</td>\n",
       "      <td>1172</td>\n",
       "      <td>214</td>\n",
       "      <td>1169</td>\n",
       "      <td>219</td>\n",
       "      <td>1162</td>\n",
       "      <td>POLYGON ((1314208.406 1178220.110, 1314211.847...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201</td>\n",
       "      <td>2</td>\n",
       "      <td>0312</td>\n",
       "      <td>2010312</td>\n",
       "      <td>101</td>\n",
       "      <td>3973</td>\n",
       "      <td>11.3</td>\n",
       "      <td>1124</td>\n",
       "      <td>1460</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>1190</td>\n",
       "      <td>1290</td>\n",
       "      <td>54</td>\n",
       "      <td>1194</td>\n",
       "      <td>1343</td>\n",
       "      <td>1152</td>\n",
       "      <td>1373</td>\n",
       "      <td>1170</td>\n",
       "      <td>1345</td>\n",
       "      <td>POLYGON ((1432565.993 851290.217, 1432575.099 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>351</td>\n",
       "      <td>4</td>\n",
       "      <td>0003</td>\n",
       "      <td>3510003</td>\n",
       "      <td>176</td>\n",
       "      <td>626</td>\n",
       "      <td>1.1</td>\n",
       "      <td>412</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>387</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>390</td>\n",
       "      <td>29</td>\n",
       "      <td>382</td>\n",
       "      <td>32</td>\n",
       "      <td>386</td>\n",
       "      <td>31</td>\n",
       "      <td>POLYGON ((1602738.373 1008175.555, 1602745.401...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>181</td>\n",
       "      <td>4</td>\n",
       "      <td>0304</td>\n",
       "      <td>1810304</td>\n",
       "      <td>91</td>\n",
       "      <td>3058</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1290</td>\n",
       "      <td>676</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>1331</td>\n",
       "      <td>597</td>\n",
       "      <td>39</td>\n",
       "      <td>1335</td>\n",
       "      <td>632</td>\n",
       "      <td>1339</td>\n",
       "      <td>617</td>\n",
       "      <td>1347</td>\n",
       "      <td>619</td>\n",
       "      <td>POLYGON ((1312523.436 1279889.507, 1312544.741...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201</td>\n",
       "      <td>2</td>\n",
       "      <td>0877</td>\n",
       "      <td>2010877</td>\n",
       "      <td>101</td>\n",
       "      <td>5743</td>\n",
       "      <td>27.1</td>\n",
       "      <td>1352</td>\n",
       "      <td>2554</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>1316</td>\n",
       "      <td>2466</td>\n",
       "      <td>105</td>\n",
       "      <td>1359</td>\n",
       "      <td>2516</td>\n",
       "      <td>1379</td>\n",
       "      <td>2495</td>\n",
       "      <td>1382</td>\n",
       "      <td>2490</td>\n",
       "      <td>POLYGON ((1409146.792 864246.161, 1409155.944 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CNTY  COLOR  PREC   PCTKEY  CNTYKEY  G20VR  G20SSVR  G20PRERTRU  \\\n",
       "0   113      7  1104  1131104       57   2745     39.5         221   \n",
       "1   201      2  0312  2010312      101   3973     11.3        1124   \n",
       "2   351      4  0003  3510003      176    626      1.1         412   \n",
       "3   181      4  0304  1810304       91   3058      4.5        1290   \n",
       "4   201      2  0877  2010877      101   5743     27.1        1352   \n",
       "\n",
       "   G20PREDBID  G20PRELJOR  ...  G20SSCRBUS  G20SSCDTRI  G20SSCLOXF  \\\n",
       "0        1173           7  ...         195        1157          32   \n",
       "1        1460          21  ...        1190        1290          54   \n",
       "2          28           0  ...         387          28           5   \n",
       "3         676          29  ...        1331         597          39   \n",
       "4        2554          43  ...        1316        2466         105   \n",
       "\n",
       "   G20SCCRRIC  G20SCCDFRI  G20SCCRYEA  G20SCCDCLI  G20SCCRNEW  G20SCCDBIR  \\\n",
       "0         216        1172         214        1169         219        1162   \n",
       "1        1194        1343        1152        1373        1170        1345   \n",
       "2         390          29         382          32         386          31   \n",
       "3        1335         632        1339         617        1347         619   \n",
       "4        1359        2516        1379        2495        1382        2490   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((1314208.406 1178220.110, 1314211.847...  \n",
       "1  POLYGON ((1432565.993 851290.217, 1432575.099 ...  \n",
       "2  POLYGON ((1602738.373 1008175.555, 1602745.401...  \n",
       "3  POLYGON ((1312523.436 1279889.507, 1312544.741...  \n",
       "4  POLYGON ((1409146.792 864246.161, 1409155.944 ...  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/tx_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CNTY</th>\n",
       "      <th>COLOR</th>\n",
       "      <th>PREC</th>\n",
       "      <th>PCTKEY</th>\n",
       "      <th>CNTYKEY</th>\n",
       "      <th>G20VR</th>\n",
       "      <th>G20SSVR</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>...</th>\n",
       "      <th>G20SSCRBUS</th>\n",
       "      <th>G20SSCDTRI</th>\n",
       "      <th>G20SSCLOXF</th>\n",
       "      <th>G20SCCRRIC</th>\n",
       "      <th>G20SCCDFRI</th>\n",
       "      <th>G20SCCRYEA</th>\n",
       "      <th>G20SCCDCLI</th>\n",
       "      <th>G20SCCRNEW</th>\n",
       "      <th>G20SCCDBIR</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>113</td>\n",
       "      <td>7</td>\n",
       "      <td>1104</td>\n",
       "      <td>1131104</td>\n",
       "      <td>57</td>\n",
       "      <td>2745</td>\n",
       "      <td>39.5</td>\n",
       "      <td>221</td>\n",
       "      <td>1173</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>195</td>\n",
       "      <td>1157</td>\n",
       "      <td>32</td>\n",
       "      <td>216</td>\n",
       "      <td>1172</td>\n",
       "      <td>214</td>\n",
       "      <td>1169</td>\n",
       "      <td>219</td>\n",
       "      <td>1162</td>\n",
       "      <td>POLYGON ((-96.64136 32.73404, -96.64136 32.733...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201</td>\n",
       "      <td>2</td>\n",
       "      <td>0312</td>\n",
       "      <td>2010312</td>\n",
       "      <td>101</td>\n",
       "      <td>3973</td>\n",
       "      <td>11.3</td>\n",
       "      <td>1124</td>\n",
       "      <td>1460</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>1190</td>\n",
       "      <td>1290</td>\n",
       "      <td>54</td>\n",
       "      <td>1194</td>\n",
       "      <td>1343</td>\n",
       "      <td>1152</td>\n",
       "      <td>1373</td>\n",
       "      <td>1170</td>\n",
       "      <td>1345</td>\n",
       "      <td>POLYGON ((-95.51892 29.74333, -95.51883 29.743...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>351</td>\n",
       "      <td>4</td>\n",
       "      <td>0003</td>\n",
       "      <td>3510003</td>\n",
       "      <td>176</td>\n",
       "      <td>626</td>\n",
       "      <td>1.1</td>\n",
       "      <td>412</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>387</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>390</td>\n",
       "      <td>29</td>\n",
       "      <td>382</td>\n",
       "      <td>32</td>\n",
       "      <td>386</td>\n",
       "      <td>31</td>\n",
       "      <td>POLYGON ((-93.66637 31.08457, -93.66630 31.084...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>181</td>\n",
       "      <td>4</td>\n",
       "      <td>0304</td>\n",
       "      <td>1810304</td>\n",
       "      <td>91</td>\n",
       "      <td>3058</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1290</td>\n",
       "      <td>676</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>1331</td>\n",
       "      <td>597</td>\n",
       "      <td>39</td>\n",
       "      <td>1335</td>\n",
       "      <td>632</td>\n",
       "      <td>1339</td>\n",
       "      <td>617</td>\n",
       "      <td>1347</td>\n",
       "      <td>619</td>\n",
       "      <td>POLYGON ((-96.62623 33.65217, -96.62603 33.651...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201</td>\n",
       "      <td>2</td>\n",
       "      <td>0877</td>\n",
       "      <td>2010877</td>\n",
       "      <td>101</td>\n",
       "      <td>5743</td>\n",
       "      <td>27.1</td>\n",
       "      <td>1352</td>\n",
       "      <td>2554</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>1316</td>\n",
       "      <td>2466</td>\n",
       "      <td>105</td>\n",
       "      <td>1359</td>\n",
       "      <td>2516</td>\n",
       "      <td>1379</td>\n",
       "      <td>2495</td>\n",
       "      <td>1382</td>\n",
       "      <td>2490</td>\n",
       "      <td>POLYGON ((-95.75613 29.86868, -95.75604 29.868...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CNTY  COLOR  PREC   PCTKEY  CNTYKEY  G20VR  G20SSVR  G20PRERTRU  \\\n",
       "0   113      7  1104  1131104       57   2745     39.5         221   \n",
       "1   201      2  0312  2010312      101   3973     11.3        1124   \n",
       "2   351      4  0003  3510003      176    626      1.1         412   \n",
       "3   181      4  0304  1810304       91   3058      4.5        1290   \n",
       "4   201      2  0877  2010877      101   5743     27.1        1352   \n",
       "\n",
       "   G20PREDBID  G20PRELJOR  ...  G20SSCRBUS  G20SSCDTRI  G20SSCLOXF  \\\n",
       "0        1173           7  ...         195        1157          32   \n",
       "1        1460          21  ...        1190        1290          54   \n",
       "2          28           0  ...         387          28           5   \n",
       "3         676          29  ...        1331         597          39   \n",
       "4        2554          43  ...        1316        2466         105   \n",
       "\n",
       "   G20SCCRRIC  G20SCCDFRI  G20SCCRYEA  G20SCCDCLI  G20SCCRNEW  G20SCCDBIR  \\\n",
       "0         216        1172         214        1169         219        1162   \n",
       "1        1194        1343        1152        1373        1170        1345   \n",
       "2         390          29         382          32         386          31   \n",
       "3        1335         632        1339         617        1347         619   \n",
       "4        1359        2516        1379        2495        1382        2490   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-96.64136 32.73404, -96.64136 32.733...  \n",
       "1  POLYGON ((-95.51892 29.74333, -95.51883 29.743...  \n",
       "2  POLYGON ((-93.66637 31.08457, -93.66630 31.084...  \n",
       "3  POLYGON ((-96.62623 33.65217, -96.62603 33.651...  \n",
       "4  POLYGON ((-95.75613 29.86868, -95.75604 29.868...  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #TX's precinct file in wrong CRS\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9014 0.5283093020704941 11149851 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        plotPoly(vtdGeom[p])\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "#isSkippedTract = [0]*nTracts  #done above; already skipped two lakeshore tracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n",
      "working on tract 1800\n",
      "working on tract 2000\n",
      "working on tract 2200\n",
      "working on tract 2400\n",
      "working on tract 2600\n",
      "working on tract 2800\n",
      "working on tract 3000\n",
      "working on tract 3200\n",
      "working on tract 3400\n",
      "working on tract 3600\n",
      "working on tract 3800\n",
      "working on tract 4000\n",
      "working on tract 4200\n",
      "working on tract 4400\n",
      "working on tract 4600\n",
      "working on tract 4800\n",
      "working on tract 5000\n",
      "working on tract 5200\n",
      "working on tract 5400\n",
      "working on tract 5600\n",
      "working on tract 5800\n",
      "working on tract 6000\n",
      "working on tract 6200\n",
      "working on tract 6400\n",
      "working on tract 6600\n",
      "working on tract 6800\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic TX map\n",
    "tractMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        uncomment = \"if-redone\"\n",
    "        tractMAP = tractMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "plotPoly(tractMAP)\n",
    "pt1 = Point(-103.2,34.8)\n",
    "pt2 = Point(-100,34.8)\n",
    "pt3 = Point(-100.,36.7)\n",
    "pt4 = Point(-103.2,36.7)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #clip western panhandle\n",
    "plotPoly(clipPoly)\n",
    "\n",
    "plt.show()\n",
    "wholeMAP = tractMAP  #in case we later slice parts out\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ae937095-879a-464c-9e7e-aa22cca2f41f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  115 tracts w pop 399223.0  to reassign to tracts...\n",
      "[2005, 2006, 2007, 2122, 2156, 2157, 2751, 4210, 4211, 4212, 4479, 5932, 6298]\n",
      "I have flagged  172 precincts to reassign to precincts...\n",
      "[266, 267, 1074, 1080, 1337, 1846, 1847, 3225, 3226, 3227, 3648, 3649, 3650, 3651, 3652, 3653, 3654, 3673, 3947, 3950, 3997, 3998, 3999, 4000, 4001, 4002, 4003, 4004, 4005, 4621, 4622, 4623, 4624, 5043, 5044, 5045, 5046, 5047, 7124, 7125, 7256]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.4: # and tractGeom[t].centroid.y < 44.7:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        isSkippedPrecinct[p] = 1\n",
    "        if cutPrecinctList == [-99999]:\n",
    "            cutPrecinctList = [p]\n",
    "        else:\n",
    "            cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.4 : #and vtdGeom[p].centroid.y < 44.7:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "197ed2dd-1350-4f75-b80a-1d0049745d58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3d97a3fd-8f05-46a8-8d06-87de95acad81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "72f80c43-c059-4c29-a048-2dc46a5aa2b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  399223.0 people and  146312.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b57dcde5-7124-4277-9e83-d244c0bcdf73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#second clipPoly\n",
    "\n",
    "plotPoly(tractMAP)\n",
    "\n",
    "pt1 = Point(-98, 25.3)\n",
    "pt2 = Point(-97,25.8)\n",
    "pt3 = Point(-97.,27.5)\n",
    "pt4 = Point(-98,25.5)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #plan to clip off SW corner near Evansville\n",
    "plotPoly(clipPoly)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "63135aab-556f-4616-a14a-9095383cbc6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  90 tracts w pop 315558.0  to reassign to tracts...\n",
      "[427, 890, 1141, 1142, 1143, 1456, 1458, 1473, 1479, 1480, 1490, 1491, 1493, 1496, 1505, 1506, 1507, 1508, 1513, 1514, 1523, 1525, 1526, 1527, 1534, 1548, 1556, 1557, 1560, 1561, 1562, 1566, 1567, 1568, 1569, 1570, 1571, 1578, 1594, 1600, 1601, 1603, 1760, 1764, 1765, 1766, 1820, 1822, 1823, 1824, 1825, 1826, 1833, 1835, 1836, 2018, 2028, 2148, 2149, 2228, 2230, 2232, 2247, 2248, 2249, 2250, 2251, 2272, 2731, 2806, 2811, 2831, 2896, 2897, 2962, 2981, 2982, 3001, 3102, 3269, 3270, 3516, 3868, 3869, 4089, 4090, 4258, 4259, 4517, 4520, 4521, 4522, 4571, 4649, 4998, 5001, 5003, 5004, 5005, 5014, 5017, 5019, 5084, 5086, 5089, 5090, 5093, 5095, 5105, 6428, 6488, 6489, 6688, 6689, 6690, 6691, 6692, 6725, 6812]\n",
      "I have flagged  81 precincts to reassign to precincts...\n",
      "[871, 872, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 902, 908, 1794, 1801, 1805, 1869, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2101, 2108, 2109, 2110, 2120, 2147, 2148, 2150, 2151, 2152, 2153, 2157, 2158, 2159, 2160, 2162, 2170, 2361, 2865, 2871, 2888, 2889, 2891, 2892, 2944, 3174, 3175, 3388, 3389, 3431, 3433, 3553, 3554, 3589, 3590, 3591, 3592, 3593, 3594, 3625, 3628, 3630, 4318, 4319, 4320, 4324, 4325, 4326, 4327, 4328, 4329, 4343, 4344, 4345, 4350, 4751, 4752, 4901, 4902, 4903, 4904, 4909, 4911, 6022, 6023, 6025, 6139, 6140, 6776, 6777, 6778, 6779, 6780, 6981, 6999, 8669, 8883, 8884, 8885, 8886, 8887, 8888, 8889, 8890, 8891, 8892, 8893, 8894, 8896, 8897, 8898, 8899, 8900, 8901, 8902, 8937, 8938, 8939, 8940, 8941, 8942, 8944, 8945]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.4: # and tractGeom[t].centroid.y < 44.7:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        isSkippedPrecinct[p] = 1\n",
    "        if cutPrecinctList == [-99999]:\n",
    "            cutPrecinctList = [p]\n",
    "        else:\n",
    "            cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.4 : #and vtdGeom[p].centroid.y < 44.7:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "401d8e76-30f7-4cdc-957d-4297a27ae92c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5310f805-77ac-482a-bca7-994107c13bbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9a0f0caa-a56d-4f9d-82d4-c544b67fae66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  315558.0 people and  86675.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e9000e13-7f14-45ba-8bc9-c5c38572e5d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-106.229  = x\n",
      "-106.228  = x\n",
      "-106.22699999999999  = x\n",
      "-106.22599999999998  = x\n",
      "-106.22499999999998  = x\n",
      "-106.22399999999998  = x\n",
      "-106.22299999999997  = x\n",
      "-106.22199999999997  = x\n",
      "final population, eastX are 767998.7439712149 -106.22199999999997\n"
     ]
    }
   ],
   "source": [
    "#Now, special for El Paso - we will simply cut out an El Paso district, and make a 37-district map\n",
    "#Let's first determine where a vertical cut will create the correct district size\n",
    "avgDistrictPop = 29145505/38.\n",
    "westPop = 0.\n",
    "dx = 0.001\n",
    "westX = -107\n",
    "startX = -106.23\n",
    "northY = 32.1\n",
    "southY = 30\n",
    "pt1 = Point(westX,northY)\n",
    "pt2 = Point(westX,southY)\n",
    "x = startX\n",
    "while westPop < avgDistrictPop:\n",
    "    westPop = 0.\n",
    "    x = x + dx\n",
    "    print(x,\" = x\")\n",
    "    pt3 = Point(x,southY)\n",
    "    pt4 = Point(x,northY)\n",
    "    clipPoly = Polygon([pt1,pt2,pt3,pt4])\n",
    "    for t in range(nTracts):\n",
    "        if clipPoly.intersects(tractGeom[t]):\n",
    "            fraction = clipPoly.intersection(tractGeom[t]).area / tractGeom[t].area\n",
    "            westPop += fraction * tractPop[t]\n",
    "print(\"final population, eastX are\",westPop,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1db86b67-c525-4b65-80e1-626810ce86ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, grab the El Paso pop out of the map, diminish tract and precinct pop's by this amount\n",
    "elPasoTractList = [-99999]\n",
    "elPasoPrecinctList = [-99999]\n",
    "westX = -107\n",
    "eastX = -106.222\n",
    "northY = 32.1\n",
    "southY = 30\n",
    "pt1 = Point(westX,northY)\n",
    "pt2 = Point(westX,southY)\n",
    "pt3 = Point(eastX,southY)\n",
    "pt4 = Point(eastX,northY)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])\n",
    "elPasoPop = 0.\n",
    "elPasoHisp = 0.\n",
    "elPasoBlack = 0.\n",
    "elPasoTrump = 0.\n",
    "elPasoBiden = 0.\n",
    "for t in range(nTracts):\n",
    "    if clipPoly.intersects(tractGeom[t]):\n",
    "        if elPasoTractList == [-99999]:\n",
    "            elPasoTractList = [t]\n",
    "        else:\n",
    "            elPasoTractList.append(t)\n",
    "        fraction = clipPoly.intersection(tractGeom[t]).area / tractGeom[t].area\n",
    "        elPasoPop += fraction*tractPop[t]\n",
    "        elPasoHisp += fraction*tractHisp[t]\n",
    "        elPasoBlack += fraction*tractBlack[t]      \n",
    "        if fraction > 0.999 :  #completely erase from map\n",
    "            isSkippedTract[t] = 1\n",
    "            tractPop[t] = 0.\n",
    "            tractHisp[t] = 0.\n",
    "            tractBlack[t] = 0.\n",
    "        else:  #keep remnant on map\n",
    "            tractPop[t] = (1.-fraction)*tractPop[t]\n",
    "            tractHisp[t] = (1.-fraction)*tractHisp[t]\n",
    "            tractBlack[t] = (1.-fraction)*tractBlack[t]\n",
    "            \n",
    "for p in range(nPrecincts):\n",
    "    if clipPoly.intersects(vtdGeom[p]):\n",
    "        if elPasoPrecinctList == [-99999]:\n",
    "            elPasoPrecinctList = [p]\n",
    "        else:\n",
    "            elPasoPrecinctList.append(p)\n",
    "        fraction = clipPoly.intersection(vtdGeom[p]).area / vtdGeom[p].area\n",
    "        elPasoTrump += fraction*vtdTrump[p]\n",
    "        elPasoBiden += fraction*vtdBiden[p]     \n",
    "        if fraction > 0.999 :  #completely erase from map\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            vtdTrump[p] = 0.\n",
    "            vtdBiden[p] = 0.\n",
    "        else:  #keep remnant on map\n",
    "            vtdTrump[p] = (1.-fraction)*vtdTrump[p]\n",
    "            vtdBiden[p] = (1.-fraction)*vtdBiden[p]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "91ecb793-846c-4761-8de8-d45506613189",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "panhandle pct GOP, total Pop are 0.3185785943484904 767998.7439711457\n",
      "panhandle VAP Hisp, VAP Black are 0.6070262004896407 0.025354602346544384\n",
      "panhandle,remnant and total populations are 767998.7439711457 28377403 29145401.743971147\n",
      "vs. census 29,145,505\n"
     ]
    }
   ],
   "source": [
    "print(\"panhandle pct GOP, total Pop are\",elPasoTrump/(elPasoBiden+elPasoTrump),elPasoPop)\n",
    "print(\"panhandle VAP Hisp, VAP Black are\",elPasoHisp/elPasoPop,elPasoBlack/elPasoPop)\n",
    "print(\"panhandle,remnant and total populations are\",elPasoPop, np.sum(tractPop),elPasoPop+np.sum(tractPop) )\n",
    "print(\"vs. census 29,145,505\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "84c2cf09-b3ea-4fc2-a55b-62985882836b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n",
      "working on tract 1800\n",
      "working on tract 2000\n",
      "working on tract 2200\n",
      "working on tract 2400\n",
      "working on tract 2600\n",
      "working on tract 2800\n",
      "working on tract 3000\n",
      "working on tract 3200\n",
      "working on tract 3400\n",
      "working on tract 3600\n",
      "working on tract 3800\n",
      "working on tract 4000\n",
      "working on tract 4200\n",
      "working on tract 4400\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_103/1107643407.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"working on tract\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misSkippedTract\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0mtractMAP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtractMAP\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtractGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0mplotPoly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtractMAP\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/shapely/geometry/base.py\u001b[0m in \u001b[0;36munion\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    696\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    697\u001b[0m         \u001b[0;34m\"\"\"Returns the union of the geometries (Shapely geometry)\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 698\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mgeom_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimpl\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'union'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    699\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    700\u001b[0m     \u001b[0;31m# Unary predicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/shapely/topology.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, this, other, *args)\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop_prepared\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m         \u001b[0mproduct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_geom\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_geom\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mproduct\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m             err = TopologicalError(\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#rebuild tractMAP, excluding skipped / sliced tracts.  #NOT NEEDED FOR OHIO -- NO SLICED populous TRACTS\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d4538dfc-7d35-46b4-95c1-c5d93169e971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPoly(tractMAP)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3dda54ea-df02-4e11-b45c-0f90fabbfb08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 4600\n",
      "working on tract 4800\n",
      "working on tract 5000\n",
      "working on tract 5200\n",
      "working on tract 5400\n",
      "working on tract 5600\n",
      "working on tract 5800\n",
      "working on tract 6000\n",
      "working on tract 6200\n",
      "working on tract 6400\n",
      "working on tract 6600\n",
      "working on tract 6800\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in range(4559,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.15853658536585366 1394\n",
      "100 0.7462981243830207 1013\n",
      "200 0.4549152542372881 1475\n",
      "300 0.5378548895899053 1902\n",
      "400 0.14534883720930233 172\n",
      "500 0.2322357019064125 577\n",
      "600 0.0 0\n",
      "700 0.05802047781569966 2051\n",
      "800 0.3628620102214651 1761\n",
      "900 0.38009049773755654 221\n",
      "1000 0.47530864197530864 162\n",
      "1100 0.20555555555555555 180\n",
      "1200 0.4967860422405877 1089\n",
      "1300 0.8850931677018633 322\n",
      "1400 0.3557692307692308 416\n",
      "1500 0.0 0\n",
      "1600 0.05128205128205128 273\n",
      "1700 0.8303785337805463 2087\n",
      "1800 0.3882783882783883 273\n",
      "1900 0.2779220779220779 385\n",
      "2000 0.3230240549828179 873\n",
      "2100 0.0 2\n",
      "2200 0.8342541436464088 543\n",
      "2300 0.6391055642225689 1923\n",
      "2400 0.6925157799819658 1109\n",
      "2500 0.9166666666666666 156\n",
      "2600 0.7931350114416476 2185\n",
      "2700 0.41325536062378165 1026\n",
      "2800 0.5706695005313497 941\n",
      "2900 0.3081232492997199 357\n",
      "3000 0.8918918918918919 407\n",
      "3100 0.9157894736842105 95\n",
      "3200 0.8288590604026845 298\n",
      "3300 0.8092672413793104 928\n",
      "3400 0.23083067092651757 1252\n",
      "3500 0.7205673758865249 705\n",
      "3600 0.31088082901554404 193\n",
      "3700 0.9110198494182067 1461\n",
      "3800 0.0416243654822335 985\n",
      "3900 0.7738359201773836 451\n",
      "4000 0.8063560463237275 3713\n",
      "4100 0.0 0\n",
      "4200 0.5505376344086022 465\n",
      "4300 0.5061307901907357 1468\n",
      "4400 0.7210401891252955 423\n",
      "4500 0.7459095283926853 1039\n",
      "4600 0.41580756013745707 291\n",
      "4700 0.9221556886227545 167\n",
      "4800 0.8452655889145496 433\n",
      "4900 0.3607142857142857 280\n",
      "5000 0.6161137440758294 2532\n",
      "5100 0.39849624060150374 532\n",
      "5200 0.2958677685950413 605\n",
      "5300 0.16954193932183223 1681\n",
      "5400 0.3298611111111111 576\n",
      "5500 0.8311245740249905 2641\n",
      "5600 0.593939393939394 1320\n",
      "5700 0.5898795180722891 2075\n",
      "5800 0.643864229765013 1915\n",
      "5900 0.549967341606793 1531\n",
      "6000 0.8003992015968064 501\n",
      "6100 0.917910447761194 134\n",
      "6200 0.5144976399190829 1483\n",
      "6300 0.3764007910349374 1517\n",
      "6400 0.5306122448979592 1176\n",
      "6500 0.5639246778989098 1009\n",
      "6600 0.6436507936507937 2520\n",
      "6700 0.6686811233558478 2813\n",
      "6800 0.2857142857142857 56\n",
      "6900 0.2692307692307692 520\n",
      "7000 0.44881889763779526 254\n",
      "7100 0.334 1000\n",
      "7200 0.6856060606060606 1848\n",
      "7300 0.7161226508407518 2022\n",
      "7400 1.0 2\n",
      "7500 0.5852534562211982 434\n",
      "7600 0.9085365853658537 492\n",
      "7700 0.5549295774647888 1065\n",
      "7800 0.7707006369426752 942\n",
      "7900 0.35248672139063253 2071\n",
      "8000 0.7062831188493566 3963\n",
      "8100 0.4535756617937574 2531\n",
      "8200 0.2874015748031496 1270\n",
      "8300 0.8481104651162791 1376\n",
      "8400 0.39853300733496333 1636\n",
      "8500 0.28694528220594573 2321\n",
      "8600 0.0 0\n",
      "8700 0.5425925925925926 1080\n",
      "8800 0.8014934660858744 1607\n",
      "8900 0.5037878787878788 1320\n",
      "9000 0.8868481469334208 3049\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 66.05417667797525 60.39368368002312\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "#bufferDistance = 0.02  #do minor buffer for OH\n",
    "origMAP = wholeMAP    # let's keep a copy of the original map prior to buffering\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",origMAP.area,MAP.area)  #same if we didn't buffer\n",
    "x2, y2 = MAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"green\")\n",
    "if origMAP.type == tractGeom[0].type :  #single Polygon\n",
    "    x2, y2 = origMAP.exterior.xy\n",
    "    plt.plot(x2,y2,c=\"blue\")\n",
    "else:\n",
    "    for geom in origMAP.geoms:\n",
    "        x2,y2 = geom.exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  20768925 10551447 0.5169475466214156\n",
      "compare to Census 21538187 Leip has Trump + Biden = 10965776.0 0.5169475466214156\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - WILL BE OFF FOR FL WITH OUR SLICE-OUT\n",
    "censusPop = 21538187\n",
    "atlasTrump = 5668731.\n",
    "atlasBiden = 5297045.\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 28377403 28377403.0\n",
      "state Trumpers before, after slice opn are 5814024 5814024.0\n",
      "state Bideners before, after slice opn are 5095491 5095491.0\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "sumBiden = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "        sumBiden += vtdBiden[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "print(\"state Bideners before, after slice opn are\",np.sum(vtdBiden),sumBiden)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b5334311-b597-4568-88c7-eb3c9d4bdf09",
   "metadata": {},
   "outputs": [],
   "source": [
    "#FAIL here with a bad precinct when building grid map in Georgia.  See below two blocks for fix and re-run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2f6c72cd-90d8-489d-9b15-d4278ba17b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 187 grids for 37 districts\n",
      "starting grid no 0 at x = -106.05463997058824, y = 26.425645545454547 \n",
      "starting grid no 5 at x = -106.05463997058821, y = 30.388851000000003 \n",
      "starting grid no 10 at x = -106.05463997058821, y = 34.35205645454545 \n",
      "starting grid no 15 at x = -105.29423991176469, y = 29.59620990909091 \n",
      "starting grid no 20 at x = -105.29423991176469, y = 33.55941536363636 \n",
      "starting grid no 25 at x = -104.53383985294116, y = 28.803568818181816 \n",
      "starting grid no 30 at x = -104.53383985294116, y = 32.766774272727275 \n",
      "starting grid no 35 at x = -103.77343979411765, y = 28.01092772727273 \n",
      "starting grid no 40 at x = -103.77343979411765, y = 31.974133181818182 \n",
      "starting grid no 45 at x = -103.01303973529411, y = 27.218286636363636 \n",
      "starting grid no 50 at x = -103.01303973529411, y = 31.18149209090909 \n",
      "starting grid no 55 at x = -102.25263967647057, y = 26.425645545454543 \n",
      "starting grid no 60 at x = -102.25263967647058, y = 30.388851000000003 \n",
      "starting grid no 65 at x = -102.25263967647058, y = 34.352056454545455 \n",
      "starting grid no 70 at x = -101.49223961764707, y = 29.596209909090902 \n",
      "starting grid no 75 at x = -101.49223961764706, y = 33.559415363636354 \n",
      "starting grid no 80 at x = -100.73183955882351, y = 28.803568818181816 \n",
      "starting grid no 85 at x = -100.73183955882352, y = 32.766774272727275 \n",
      "starting grid no 90 at x = -99.9714395, y = 28.01092772727273 \n",
      "starting grid no 95 at x = -99.9714395, y = 31.974133181818182 \n",
      "starting grid no 100 at x = -99.21103944117647, y = 27.218286636363636 \n",
      "starting grid no 105 at x = -99.21103944117647, y = 31.18149209090909 \n",
      "starting grid no 110 at x = -98.45063938235293, y = 26.425645545454543 \n",
      "starting grid no 115 at x = -98.45063938235293, y = 30.388851000000003 \n",
      "starting grid no 120 at x = -98.45063938235293, y = 34.352056454545455 \n",
      "starting grid no 125 at x = -97.69023932352941, y = 29.596209909090902 \n",
      "starting grid no 130 at x = -97.69023932352943, y = 33.559415363636354 \n",
      "starting grid no 135 at x = -96.92983926470588, y = 28.803568818181816 \n",
      "starting grid no 140 at x = -96.92983926470586, y = 32.766774272727275 \n",
      "starting grid no 145 at x = -96.16943920588236, y = 28.01092772727273 \n",
      "starting grid no 150 at x = -96.16943920588236, y = 31.974133181818182 \n",
      "starting grid no 155 at x = -95.40903914705882, y = 27.218286636363636 \n",
      "starting grid no 160 at x = -95.4090391470588, y = 31.18149209090909 \n",
      "starting grid no 165 at x = -94.64863908823529, y = 26.425645545454543 \n",
      "starting grid no 170 at x = -94.64863908823529, y = 30.388851000000003 \n",
      "starting grid no 175 at x = -94.64863908823529, y = 34.352056454545455 \n",
      "starting grid no 180 at x = -93.88823902941176, y = 29.596209909090902 \n",
      "starting grid no 185 at x = -93.88823902941176, y = 33.559415363636354 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 376655.15043830895 8958062.39047968 125.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 37   #Texas - El Paso\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 2.5  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if (notPoly[t] == 0) :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                #print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if (notPolyVTD[p] == 0) :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c86e79b4-f9c9-4de1-9b8a-374fc9761f02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid 0 is empty\n",
      "grid 1 is empty\n",
      "grid 2 is empty\n",
      "grid 3 is empty\n",
      "grid 4 is empty\n",
      "grid 5 is empty\n",
      "grid 8 is empty\n",
      "grid 9 is empty\n",
      "grid 10 is empty\n",
      "grid 11 is empty\n",
      "grid 12 is empty\n",
      "grid 13 is empty\n",
      "grid 14 is empty\n",
      "grid 15 is empty\n",
      "grid 19 is empty\n",
      "grid 20 is empty\n",
      "grid 21 is empty\n",
      "grid 22 is empty\n",
      "grid 23 is empty\n",
      "grid 24 is empty\n",
      "grid 25 is empty\n",
      "grid 30 is empty\n",
      "grid 31 is empty\n",
      "grid 32 is empty\n",
      "grid 33 is empty\n",
      "grid 34 is empty\n",
      "grid 35 is empty\n",
      "grid 41 is empty\n",
      "grid 42 is empty\n",
      "grid 43 is empty\n",
      "grid 44 is empty\n",
      "grid 45 is empty\n",
      "grid 46 is empty\n",
      "grid 55 is empty\n",
      "grid 56 is empty\n",
      "grid 57 is empty\n",
      "grid 58 is empty\n",
      "grid 66 is empty\n",
      "grid 67 is empty\n",
      "grid 68 is empty\n",
      "grid 69 is empty\n",
      "grid 77 is empty\n",
      "grid 78 is empty\n",
      "grid 79 is empty\n",
      "grid 88 is empty\n",
      "grid 89 is empty\n",
      "grid 142 is empty\n",
      "grid 143 is empty\n",
      "grid 144 is empty\n",
      "grid 153 is empty\n",
      "grid 154 is empty\n",
      "grid 155 is empty\n",
      "grid 156 is empty\n",
      "grid 165 is empty\n",
      "grid 166 is empty\n",
      "grid 167 is empty\n",
      "grid 175 is empty\n",
      "grid 176 is empty\n",
      "grid 177 is empty\n",
      "grid 178 is empty\n",
      "grid 179 is empty\n",
      "grid 186 is empty\n"
     ]
    }
   ],
   "source": [
    "isEmptyGrid = [0]*nGrids\n",
    "for nG in range(nGrids):\n",
    "    if gridPop[nG] == 0:\n",
    "        isEmptyGrid[nG] = 1\n",
    "        print(\"grid\",nG,\"is empty\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0660127104807588e-09 1.2073419000004009e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "minTractPop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1 6.0 3 73.6 1.5302 260721.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1 7.0 3 73.6 1.4841 260721.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1 1 461582.7330872055 2.6194\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1 1 51431.54903066362 1.3097\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1 1 463960.1007717459 2.6865\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1 1 240599.9681318796 1.9981\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1 1 443038.34698867775 2.3423\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1 1 385515.45780401386 2.1702\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1 1 285676.5715702813 2.0841\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1 1 264146.21278016106 2.0411\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1 1 255144.43793002795 2.0196\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5 1 460485.0356642895 2.5471\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5 1 48270.20244092203 1.2736\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5 1 464503.5262571579 2.6618\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5 1 217193.84954172996 1.9677\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5 1 443021.2728574109 2.3148\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5 1 366724.29768841504 2.1412\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5 1 277991.6109106028 2.0545\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5 1 257394.0346134449 2.0111\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5 1 245016.04775214414 1.9894\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 7 5.0 3 73.1 1.8346 282249.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 7 6.0 3 73.1 1.6839 282249.5\n",
      "I am working on tract number 20 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 39 0 848662.0625635537 3.9008\n",
      "7 yoyos for tract,wedge,wedgePop,r= 39 0 341029.4412194505 1.9504\n",
      "8 yoyos for tract,wedge,wedgePop,r= 39 0 20468.51449828624 0.9752\n",
      "8 yoyos for tract,wedge,wedgePop,r= 39 0 44336.594325391074 1.545\n",
      "9 yoyos for tract,wedge,wedgePop,r= 39 0 330693.0609543713 1.8298\n",
      "9 yoyos for tract,wedge,wedgePop,r= 39 0 196915.9003159452 1.6874\n",
      "10 yoyos for tract,wedge,wedgePop,r= 39 0 79650.91907131171 1.6162\n",
      "10 yoyos for tract,wedge,wedgePop,r= 39 0 138524.96723332512 1.6518\n",
      "10 yoyos for tract,wedge,wedgePop,r= 39 0 167832.99969833446 1.6696\n",
      "10 yoyos for tract,wedge,wedgePop,r= 39 0 181676.4879463329 1.6785\n",
      "I am working on tract number 40 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 47 7.0 1 90.0 6.9242 228103.1\n",
      "I am working on tract number 60 of 6896 tracts\n",
      "I am working on tract number 80 of 6896 tracts\n",
      "I am working on tract number 100 of 6896 tracts\n",
      "I am working on tract number 120 of 6896 tracts\n",
      "I am working on tract number 140 of 6896 tracts\n",
      "I am working on tract number 160 of 6896 tracts\n",
      "I am working on tract number 180 of 6896 tracts\n",
      "I am working on tract number 200 of 6896 tracts\n",
      "I am working on tract number 220 of 6896 tracts\n",
      "I am working on tract number 240 of 6896 tracts\n",
      "I am working on tract number 260 of 6896 tracts\n",
      "I am working on tract number 280 of 6896 tracts\n",
      "I am working on tract number 300 of 6896 tracts\n",
      "I am working on tract number 320 of 6896 tracts\n",
      "I am working on tract number 340 of 6896 tracts\n",
      "I am working on tract number 360 of 6896 tracts\n",
      "I am working on tract number 380 of 6896 tracts\n",
      "I am working on tract number 400 of 6896 tracts\n",
      "I am working on tract number 420 of 6896 tracts\n",
      "I am working on tract number 440 of 6896 tracts\n",
      "I am working on tract number 460 of 6896 tracts\n",
      "I am working on tract number 480 of 6896 tracts\n",
      "I am working on tract number 500 of 6896 tracts\n",
      "I am working on tract number 520 of 6896 tracts\n",
      "I am working on tract number 540 of 6896 tracts\n",
      "I am working on tract number 560 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 561 5.0 0 90.0 0.6227 181893.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 561 6.0 0 90.0 0.6476 181893.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 561 0 288098.2695325273 1.8952\n",
      "we have 2 non-opposing shorted wedges for tract no 575\n",
      "we have 2 non-opposing shorted wedges for tract no 576\n",
      "I am working on tract number 580 of 6896 tracts\n",
      "I am working on tract number 600 of 6896 tracts\n",
      "I am working on tract number 620 of 6896 tracts\n",
      "I am working on tract number 640 of 6896 tracts\n",
      "I am working on tract number 660 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 662 1 472741.6964263375 3.4815\n",
      "8 yoyos for tract,wedge,wedgePop,r= 662 1 28027.60126290482 1.7408\n",
      "9 yoyos for tract,wedge,wedgePop,r= 662 1 473476.4852359506 3.486\n",
      "10 yoyos for tract,wedge,wedgePop,r= 662 1 88033.03020609007 2.6134\n",
      "11 yoyos for tract,wedge,wedgePop,r= 662 1 389618.5240922273 3.0497\n",
      "11 yoyos for tract,wedge,wedgePop,r= 662 1 350627.88241210306 2.8315\n",
      "11 yoyos for tract,wedge,wedgePop,r= 662 1 238688.09248296128 2.7224\n",
      "12 yoyos for tract,wedge,wedgePop,r= 662 1 141670.17150055512 2.6679\n",
      "I am working on tract number 680 of 6896 tracts\n",
      "I am working on tract number 700 of 6896 tracts\n",
      "I am working on tract number 720 of 6896 tracts\n",
      "I am working on tract number 740 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 752 1 241442.53365986474 1.046\n",
      "8 yoyos for tract,wedge,wedgePop,r= 752 1 32299.676771363243 0.523\n",
      "9 yoyos for tract,wedge,wedgePop,r= 752 1 290454.64628699224 1.1263\n",
      "10 yoyos for tract,wedge,wedgePop,r= 752 1 64341.478692487 0.8246\n",
      "10 yoyos for tract,wedge,wedgePop,r= 752 1 164268.875505251 0.9755\n",
      "11 yoyos for tract,wedge,wedgePop,r= 752 1 246031.38740444125 1.0509\n",
      "11 yoyos for tract,wedge,wedgePop,r= 752 1 212797.9031983767 1.0132\n",
      "I am working on tract number 760 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 770 3 375865.94007603073 2.7673\n",
      "8 yoyos for tract,wedge,wedgePop,r= 770 3 38197.87488421897 1.3837\n",
      "9 yoyos for tract,wedge,wedgePop,r= 770 3 346195.9323197694 2.6057\n",
      "9 yoyos for tract,wedge,wedgePop,r= 770 3 291233.2968632407 1.9947\n",
      "10 yoyos for tract,wedge,wedgePop,r= 770 3 67093.24386361451 1.6892\n",
      "10 yoyos for tract,wedge,wedgePop,r= 770 3 109040.58743904774 1.8419\n",
      "10 yoyos for tract,wedge,wedgePop,r= 770 3 182804.07702527096 1.9183\n",
      "11 yoyos for tract,wedge,wedgePop,r= 770 3 243361.37746431693 1.9565\n",
      "11 yoyos for tract,wedge,wedgePop,r= 770 3 211792.58784902975 1.9374\n",
      "11 yoyos for tract,wedge,wedgePop,r= 770 3 196771.33226478813 1.9278\n",
      "7 yoyos for tract,wedge,wedgePop,r= 772 1 383470.4967113335 2.6842\n",
      "8 yoyos for tract,wedge,wedgePop,r= 772 1 48113.99006698339 1.3421\n",
      "9 yoyos for tract,wedge,wedgePop,r= 772 1 383466.4629771831 2.6842\n",
      "9 yoyos for tract,wedge,wedgePop,r= 772 1 321323.6305872786 2.0131\n",
      "10 yoyos for tract,wedge,wedgePop,r= 772 1 68695.39887262878 1.6776\n",
      "10 yoyos for tract,wedge,wedgePop,r= 772 1 118062.02162174285 1.8454\n",
      "11 yoyos for tract,wedge,wedgePop,r= 772 1 209136.5954792286 1.9293\n",
      "12 yoyos for tract,wedge,wedgePop,r= 772 1 146598.6382881009 1.8873\n",
      "12 yoyos for tract,wedge,wedgePop,r= 772 1 176257.52797143516 1.9083\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 776 9.0 2 90.0 6.5645 256559.2\n",
      "I am working on tract number 780 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 788 1 398158.7968187544 2.8252\n",
      "8 yoyos for tract,wedge,wedgePop,r= 788 1 176900.37049624018 1.4126\n",
      "9 yoyos for tract,wedge,wedgePop,r= 788 1 398304.669448114 2.8286\n",
      "10 yoyos for tract,wedge,wedgePop,r= 788 1 238435.02406624632 2.1206\n",
      "11 yoyos for tract,wedge,wedgePop,r= 788 1 312699.35824151424 2.4746\n",
      "12 yoyos for tract,wedge,wedgePop,r= 788 1 244625.7421671933 2.2976\n",
      "12 yoyos for tract,wedge,wedgePop,r= 788 1 250843.35468360657 2.3861\n",
      "12 yoyos for tract,wedge,wedgePop,r= 788 1 263414.5465883754 2.4304\n",
      "13 yoyos for tract,wedge,wedgePop,r= 788 1 284068.9899951076 2.4525\n",
      "7 yoyos for tract,wedge,wedgePop,r= 799 1 288402.3913281065 2.3148\n",
      "8 yoyos for tract,wedge,wedgePop,r= 799 1 119641.8128188044 1.1574\n",
      "9 yoyos for tract,wedge,wedgePop,r= 799 1 292306.14605722413 2.3996\n",
      "10 yoyos for tract,wedge,wedgePop,r= 799 1 150375.26449669417 1.7785\n",
      "11 yoyos for tract,wedge,wedgePop,r= 799 1 252530.44002014343 2.0891\n",
      "12 yoyos for tract,wedge,wedgePop,r= 799 1 164637.64804181748 1.9338\n",
      "13 yoyos for tract,wedge,wedgePop,r= 799 1 223331.59963392868 2.0114\n",
      "I am working on tract number 800 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 810 2 457611.63544186956 2.3075\n",
      "8 yoyos for tract,wedge,wedgePop,r= 810 2 24000.753509996983 1.1538\n",
      "9 yoyos for tract,wedge,wedgePop,r= 810 2 508024.72053372744 2.4174\n",
      "9 yoyos for tract,wedge,wedgePop,r= 810 2 363438.88418447145 1.7856\n",
      "10 yoyos for tract,wedge,wedgePop,r= 810 2 85036.34111271286 1.4697\n",
      "11 yoyos for tract,wedge,wedgePop,r= 810 2 334119.99371735397 1.6276\n",
      "11 yoyos for tract,wedge,wedgePop,r= 810 2 231565.88116255763 1.5487\n",
      "12 yoyos for tract,wedge,wedgePop,r= 810 2 151253.11807941657 1.5092\n",
      "12 yoyos for tract,wedge,wedgePop,r= 810 2 187351.4555891863 1.5289\n",
      "13 yoyos for tract,wedge,wedgePop,r= 810 2 213392.04185726546 1.5388\n",
      "13 yoyos for tract,wedge,wedgePop,r= 810 2 199962.26044332067 1.5339\n",
      "7 yoyos for tract,wedge,wedgePop,r= 815 3 691041.8781601924 3.1921\n",
      "8 yoyos for tract,wedge,wedgePop,r= 815 3 34239.680217016605 1.5961\n",
      "9 yoyos for tract,wedge,wedgePop,r= 815 3 450525.55263360415 2.8697\n",
      "9 yoyos for tract,wedge,wedgePop,r= 815 3 374093.15499200474 2.2329\n",
      "9 yoyos for tract,wedge,wedgePop,r= 815 3 345996.2356235372 1.9145\n",
      "10 yoyos for tract,wedge,wedgePop,r= 815 3 140745.3741645899 1.7553\n",
      "11 yoyos for tract,wedge,wedgePop,r= 815 3 282093.80233247485 1.8349\n",
      "11 yoyos for tract,wedge,wedgePop,r= 815 3 211825.33136436244 1.7951\n",
      "12 yoyos for tract,wedge,wedgePop,r= 815 3 175016.52718223975 1.7752\n",
      "7 yoyos for tract,wedge,wedgePop,r= 817 3 342053.1790786666 1.9247\n",
      "8 yoyos for tract,wedge,wedgePop,r= 817 3 31260.16101022344 0.9623\n",
      "9 yoyos for tract,wedge,wedgePop,r= 817 3 418494.29966034845 2.6943\n",
      "9 yoyos for tract,wedge,wedgePop,r= 817 3 197730.33958182269 1.8283\n",
      "10 yoyos for tract,wedge,wedgePop,r= 817 3 41801.518442887376 1.3953\n",
      "10 yoyos for tract,wedge,wedgePop,r= 817 3 48926.090183747474 1.6118\n",
      "10 yoyos for tract,wedge,wedgePop,r= 817 3 63540.79030966363 1.7201\n",
      "10 yoyos for tract,wedge,wedgePop,r= 817 3 106386.60366892282 1.7742\n",
      "10 yoyos for tract,wedge,wedgePop,r= 817 3 153995.5880847027 1.8013\n",
      "10 yoyos for tract,wedge,wedgePop,r= 817 3 176737.1922991921 1.8148\n",
      "10 yoyos for tract,wedge,wedgePop,r= 817 3 187861.680260761 1.8216\n",
      "7 yoyos for tract,wedge,wedgePop,r= 818 2 526766.4185854554 2.9468\n",
      "8 yoyos for tract,wedge,wedgePop,r= 818 2 30835.01671003329 1.4734\n",
      "9 yoyos for tract,wedge,wedgePop,r= 818 2 524373.5957809862 2.9381\n",
      "9 yoyos for tract,wedge,wedgePop,r= 818 2 379748.4793032403 2.2057\n",
      "10 yoyos for tract,wedge,wedgePop,r= 818 2 74900.14189029025 1.8396\n",
      "11 yoyos for tract,wedge,wedgePop,r= 818 2 339070.4409205455 2.0226\n",
      "11 yoyos for tract,wedge,wedgePop,r= 818 2 206887.9660323356 1.9311\n",
      "12 yoyos for tract,wedge,wedgePop,r= 818 2 121809.48501566112 1.8853\n",
      "12 yoyos for tract,wedge,wedgePop,r= 818 2 164460.86560441845 1.9082\n",
      "12 yoyos for tract,wedge,wedgePop,r= 818 2 186229.0509314406 1.9197\n",
      "13 yoyos for tract,wedge,wedgePop,r= 818 2 196887.40786240023 1.9254\n",
      "I am working on tract number 820 of 6896 tracts\n",
      "I am working on tract number 840 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 843\n",
      "7 yoyos for tract,wedge,wedgePop,r= 844 3 1769956.7262145344 9.5238\n",
      "7 yoyos for tract,wedge,wedgePop,r= 844 3 1132991.9516677018 4.7619\n",
      "8 yoyos for tract,wedge,wedgePop,r= 844 3 26025.219408358447 2.381\n",
      "9 yoyos for tract,wedge,wedgePop,r= 844 3 469126.8114035057 3.9371\n",
      "9 yoyos for tract,wedge,wedgePop,r= 844 3 421882.70631268475 3.1591\n",
      "10 yoyos for tract,wedge,wedgePop,r= 844 3 49039.99376633827 2.77\n",
      "10 yoyos for tract,wedge,wedgePop,r= 844 3 80606.82761251621 2.9645\n",
      "10 yoyos for tract,wedge,wedgePop,r= 844 3 188835.86652791768 3.0618\n",
      "11 yoyos for tract,wedge,wedgePop,r= 844 3 368918.6869864349 3.1104\n",
      "11 yoyos for tract,wedge,wedgePop,r= 844 3 290771.89876028564 3.0861\n",
      "12 yoyos for tract,wedge,wedgePop,r= 844 3 245506.4603308422 3.0739\n",
      "loop31.0, tr844,wedgePops781999.1, 1949.3, 255206.8, 255301.2, 269541.8, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?05112 \n",
      "   targetWP, latest drx4 are tWP,dr, 255002.5,1.2776, 255002.5,-0.0007, 255002.5,-0.0013, 255002.5,0.0061\n",
      "13 yoyos for tract,wedge,wedgePop,r= 844 3 269541.75570338947 3.08\n",
      "loop32.0, tr844,wedgePops770684.35, 1949.3, 255206.8, 255301.2, 258227.0, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?05113 \n",
      "   targetWP, latest drx4 are tWP,dr, 255002.5,1.2776, 255002.5,-0.0007, 255002.5,-0.0013, 255002.5,-0.003\n",
      "7 yoyos for tract,wedge,wedgePop,r= 845 3 509336.91458142176 3.1997\n",
      "8 yoyos for tract,wedge,wedgePop,r= 845 3 74048.68030567357 1.5999\n",
      "9 yoyos for tract,wedge,wedgePop,r= 845 3 529405.8118004696 3.2119\n",
      "10 yoyos for tract,wedge,wedgePop,r= 845 3 81769.4836214144 2.4059\n",
      "10 yoyos for tract,wedge,wedgePop,r= 845 3 113493.83491568232 2.8089\n",
      "10 yoyos for tract,wedge,wedgePop,r= 845 3 173057.71272990425 3.0104\n",
      "10 yoyos for tract,wedge,wedgePop,r= 845 3 202847.07003939318 3.1111\n",
      "11 yoyos for tract,wedge,wedgePop,r= 845 3 386643.7280810389 3.1615\n",
      "11 yoyos for tract,wedge,wedgePop,r= 845 3 272293.77160573436 3.1363\n",
      "12 yoyos for tract,wedge,wedgePop,r= 845 3 228559.31124658248 3.1237\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 850 6.0 1 37.5 1.6631 259238.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 854 3 294025.4646941614 2.5281\n",
      "8 yoyos for tract,wedge,wedgePop,r= 854 3 151328.90849190476 1.2641\n",
      "9 yoyos for tract,wedge,wedgePop,r= 854 3 293900.4911282199 2.5262\n",
      "10 yoyos for tract,wedge,wedgePop,r= 854 3 175693.1891169461 1.8951\n",
      "10 yoyos for tract,wedge,wedgePop,r= 854 3 206235.32559420605 2.2107\n",
      "11 yoyos for tract,wedge,wedgePop,r= 854 3 281668.9540720058 2.3684\n",
      "11 yoyos for tract,wedge,wedgePop,r= 854 3 260226.74192386112 2.2896\n",
      "12 yoyos for tract,wedge,wedgePop,r= 854 3 214300.71542927972 2.2501\n",
      "12 yoyos for tract,wedge,wedgePop,r= 854 3 233689.2513926776 2.2698\n",
      "12 yoyos for tract,wedge,wedgePop,r= 854 3 247569.87362968177 2.2797\n",
      "I am working on tract number 860 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 873 2 256711.25694398984 2.7902\n",
      "8 yoyos for tract,wedge,wedgePop,r= 873 2 28464.10681773172 1.3951\n",
      "9 yoyos for tract,wedge,wedgePop,r= 873 2 258482.3469633253 2.8083\n",
      "10 yoyos for tract,wedge,wedgePop,r= 873 2 89589.01191317372 2.1017\n",
      "10 yoyos for tract,wedge,wedgePop,r= 873 2 126322.25205343847 2.455\n",
      "11 yoyos for tract,wedge,wedgePop,r= 873 2 236683.89363638186 2.6317\n",
      "12 yoyos for tract,wedge,wedgePop,r= 873 2 180782.20139009386 2.5433\n",
      "13 yoyos for tract,wedge,wedgePop,r= 873 2 222565.50539134414 2.5875\n",
      "13 yoyos for tract,wedge,wedgePop,r= 873 2 203563.84391483708 2.5654\n",
      "7 yoyos for tract,wedge,wedgePop,r= 876 1 405915.7227396503 3.9113\n",
      "8 yoyos for tract,wedge,wedgePop,r= 876 1 42024.42779284314 1.9556\n",
      "9 yoyos for tract,wedge,wedgePop,r= 876 1 410029.59118639684 3.9566\n",
      "9 yoyos for tract,wedge,wedgePop,r= 876 1 340305.367345655 2.9561\n",
      "10 yoyos for tract,wedge,wedgePop,r= 876 1 72284.64583879884 2.4559\n",
      "11 yoyos for tract,wedge,wedgePop,r= 876 1 258461.52175770592 2.706\n",
      "12 yoyos for tract,wedge,wedgePop,r= 876 1 110984.54734026536 2.5809\n",
      "12 yoyos for tract,wedge,wedgePop,r= 876 1 172896.9853859856 2.6435\n",
      "13 yoyos for tract,wedge,wedgePop,r= 876 1 222088.77832649587 2.6747\n",
      "13 yoyos for tract,wedge,wedgePop,r= 876 1 197246.4309266318 2.6591\n",
      "14 yoyos for tract,wedge,wedgePop,r= 876 1 184856.4943708439 2.6513\n",
      "I am working on tract number 880 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 889 0 983127.8204978412 6.7181\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 889 22.0 0 107.5 3.9188 983127.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 889 0 983127.8204978412 3.9188\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 889 23.0 0 107.5 2.5192 983127.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 889 0 983127.8204978412 2.5192\n",
      "7 yoyos for tract,wedge,wedgePop,r= 889 0 815878.9368468365 1.8194\n",
      "7 yoyos for tract,wedge,wedgePop,r= 889 0 390982.81610187714 1.4695\n",
      "8 yoyos for tract,wedge,wedgePop,r= 889 0 75594.9011367393 1.2946\n",
      "9 yoyos for tract,wedge,wedgePop,r= 889 0 226964.3298800868 1.382\n",
      "10 yoyos for tract,wedge,wedgePop,r= 889 0 144738.70764911763 1.3383\n",
      "10 yoyos for tract,wedge,wedgePop,r= 889 0 183978.37304397163 1.3602\n",
      "11 yoyos for tract,wedge,wedgePop,r= 889 0 205196.0726062046 1.3711\n",
      "loop31.0, tr889,wedgePops769581.95, 194539.8, 191304.9, 191897.4, 191839.8, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?11433 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,-0.0055, 191739.2,0.0007, 191739.2,-0.0005, 191739.2,-0.0005\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 890 11.0 0 90.0 2.0269 1154868.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 893 3 377535.9690187123 3.1237\n",
      "8 yoyos for tract,wedge,wedgePop,r= 893 3 39638.08291917463 1.5618\n",
      "9 yoyos for tract,wedge,wedgePop,r= 893 3 381128.5642796158 3.2646\n",
      "9 yoyos for tract,wedge,wedgePop,r= 893 3 339827.67827098654 2.4132\n",
      "10 yoyos for tract,wedge,wedgePop,r= 893 3 67015.80364824279 1.9875\n",
      "10 yoyos for tract,wedge,wedgePop,r= 893 3 91248.14679692805 2.2004\n",
      "11 yoyos for tract,wedge,wedgePop,r= 893 3 299621.09122940013 2.3068\n",
      "12 yoyos for tract,wedge,wedgePop,r= 893 3 171215.24401308497 2.2536\n",
      "13 yoyos for tract,wedge,wedgePop,r= 893 3 241788.1230361144 2.2802\n",
      "13 yoyos for tract,wedge,wedgePop,r= 893 3 206098.52499392687 2.2669\n",
      "7 yoyos for tract,wedge,wedgePop,r= 894 2 426404.256366655 3.1981\n",
      "8 yoyos for tract,wedge,wedgePop,r= 894 2 52251.75014855742 1.5991\n",
      "9 yoyos for tract,wedge,wedgePop,r= 894 2 426444.2244978039 3.199\n",
      "9 yoyos for tract,wedge,wedgePop,r= 894 2 379681.04004167655 2.399\n",
      "10 yoyos for tract,wedge,wedgePop,r= 894 2 82349.36927215842 1.9991\n",
      "11 yoyos for tract,wedge,wedgePop,r= 894 2 304233.7046904164 2.199\n",
      "12 yoyos for tract,wedge,wedgePop,r= 894 2 111170.64814770175 2.0991\n",
      "13 yoyos for tract,wedge,wedgePop,r= 894 2 204252.48561135013 2.1491\n",
      "14 yoyos for tract,wedge,wedgePop,r= 894 2 150496.6410030243 2.1241\n",
      "14 yoyos for tract,wedge,wedgePop,r= 894 2 176906.65533277724 2.1366\n",
      "I am working on tract number 900 of 6896 tracts\n",
      "I am working on tract number 920 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 924 2 1173789.2637734048 5.7786\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 924 24.0 2 452.4 3.401 1173789.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 924 2 1173789.2637734048 3.401\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 924 25.0 2 452.4 2.2123 1173789.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 924 2 1173789.2637734048 2.2123\n",
      "7 yoyos for tract,wedge,wedgePop,r= 924 2 731113.7813417106 1.6179\n",
      "8 yoyos for tract,wedge,wedgePop,r= 924 2 43895.53786719253 1.3207\n",
      "9 yoyos for tract,wedge,wedgePop,r= 924 2 378793.68669634283 1.4693\n",
      "10 yoyos for tract,wedge,wedgePop,r= 924 2 155328.75973690944 1.395\n",
      "11 yoyos for tract,wedge,wedgePop,r= 924 2 265287.2586186975 1.4321\n",
      "loop31.0, tr924,wedgePops780984.2, 191648.6, 191554.9, 205912.7, 191868.0, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?24113 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0027, 191739.2,0.0015, 191739.2,-0.0186, 191739.2,-0.0002\n",
      "11 yoyos for tract,wedge,wedgePop,r= 924 2 205912.6649732014 1.4136\n",
      "loop32.0, tr924,wedgePops754882.17, 191648.6, 191554.9, 179810.6, 191868.0, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?24113 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0027, 191739.2,0.0015, 191739.2,-0.0093, 191739.2,-0.0002\n",
      "12 yoyos for tract,wedge,wedgePop,r= 924 2 179810.63879695034 1.4043\n",
      "loop33.0, tr924,wedgePops767542.34, 191648.6, 191554.9, 192470.8, 191868.0, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?24123 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0027, 191739.2,0.0015, 191739.2,0.0046, 191739.2,-0.0002\n",
      "7 yoyos for tract,wedge,wedgePop,r= 925 2 450747.59163436934 3.4418\n",
      "8 yoyos for tract,wedge,wedgePop,r= 925 2 69660.62799272093 1.7209\n",
      "9 yoyos for tract,wedge,wedgePop,r= 925 2 454732.3327813386 3.5391\n",
      "9 yoyos for tract,wedge,wedgePop,r= 925 2 396495.46537772246 2.63\n",
      "9 yoyos for tract,wedge,wedgePop,r= 925 2 359619.5286537078 2.1755\n",
      "10 yoyos for tract,wedge,wedgePop,r= 925 2 85697.60772727604 1.9482\n",
      "11 yoyos for tract,wedge,wedgePop,r= 925 2 198497.2159890825 2.0618\n",
      "12 yoyos for tract,wedge,wedgePop,r= 925 2 101185.30710343082 2.005\n",
      "12 yoyos for tract,wedge,wedgePop,r= 925 2 138454.14206292815 2.0334\n",
      "12 yoyos for tract,wedge,wedgePop,r= 925 2 168784.39274734168 2.0476\n",
      "12 yoyos for tract,wedge,wedgePop,r= 925 2 184044.16975410478 2.0547\n",
      "7 yoyos for tract,wedge,wedgePop,r= 932 2 386032.9060600394 2.1643\n",
      "8 yoyos for tract,wedge,wedgePop,r= 932 2 102664.19477731141 1.0821\n",
      "9 yoyos for tract,wedge,wedgePop,r= 932 2 389878.9576066116 2.282\n",
      "10 yoyos for tract,wedge,wedgePop,r= 932 2 137397.10927823166 1.6821\n",
      "10 yoyos for tract,wedge,wedgePop,r= 932 2 156659.5953897803 1.982\n",
      "11 yoyos for tract,wedge,wedgePop,r= 932 2 383033.2525926939 2.132\n",
      "11 yoyos for tract,wedge,wedgePop,r= 932 2 243153.7311812144 2.057\n",
      "12 yoyos for tract,wedge,wedgePop,r= 932 2 185021.96832488838 2.0195\n",
      "13 yoyos for tract,wedge,wedgePop,r= 932 2 203474.3155073655 2.0383\n",
      "I am working on tract number 940 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 945 2 443099.2783201351 3.2469\n",
      "8 yoyos for tract,wedge,wedgePop,r= 945 2 61033.06626054004 1.6235\n",
      "9 yoyos for tract,wedge,wedgePop,r= 945 2 455925.4339666115 3.5601\n",
      "9 yoyos for tract,wedge,wedgePop,r= 945 2 393535.5985975133 2.5918\n",
      "9 yoyos for tract,wedge,wedgePop,r= 945 2 308036.6779714909 2.1076\n",
      "10 yoyos for tract,wedge,wedgePop,r= 945 2 68536.40614732332 1.8655\n",
      "10 yoyos for tract,wedge,wedgePop,r= 945 2 92938.38060182972 1.9866\n",
      "10 yoyos for tract,wedge,wedgePop,r= 945 2 187060.2314702624 2.0471\n",
      "11 yoyos for tract,wedge,wedgePop,r= 945 2 241023.4335714638 2.0774\n",
      "11 yoyos for tract,wedge,wedgePop,r= 945 2 213638.14162653527 2.0622\n",
      "11 yoyos for tract,wedge,wedgePop,r= 945 2 201968.14519817568 2.0547\n",
      "I am working on tract number 960 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 962 3 412521.52651287743 2.2508\n",
      "8 yoyos for tract,wedge,wedgePop,r= 962 3 29574.153674085625 1.1254\n",
      "9 yoyos for tract,wedge,wedgePop,r= 962 3 416844.8095154907 2.2616\n",
      "10 yoyos for tract,wedge,wedgePop,r= 962 3 179068.52106353178 1.6935\n",
      "11 yoyos for tract,wedge,wedgePop,r= 962 3 374567.55605341325 1.9776\n",
      "11 yoyos for tract,wedge,wedgePop,r= 962 3 260808.67594188044 1.8355\n",
      "11 yoyos for tract,wedge,wedgePop,r= 962 3 208314.8285521123 1.7645\n",
      "11 yoyos for tract,wedge,wedgePop,r= 962 3 198779.8127016998 1.729\n",
      "I am working on tract number 980 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 995 1 470848.275072203 1.5955\n",
      "8 yoyos for tract,wedge,wedgePop,r= 995 1 51860.99117544084 0.7978\n",
      "9 yoyos for tract,wedge,wedgePop,r= 995 1 469127.3263399287 1.568\n",
      "9 yoyos for tract,wedge,wedgePop,r= 995 1 372369.6398439286 1.1829\n",
      "10 yoyos for tract,wedge,wedgePop,r= 995 1 141681.36343246565 0.9903\n",
      "11 yoyos for tract,wedge,wedgePop,r= 995 1 278886.4898120677 1.0866\n",
      "11 yoyos for tract,wedge,wedgePop,r= 995 1 220520.1881639199 1.0385\n",
      "12 yoyos for tract,wedge,wedgePop,r= 995 1 182399.15580393202 1.0144\n",
      "I am working on tract number 1000 of 6896 tracts\n",
      "I am working on tract number 1020 of 6896 tracts\n",
      "I am working on tract number 1040 of 6896 tracts\n",
      "I am working on tract number 1060 of 6896 tracts\n",
      "I am working on tract number 1080 of 6896 tracts\n",
      "I am working on tract number 1100 of 6896 tracts\n",
      "I am working on tract number 1120 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1127 7.0 2 90.0 1.5237 663020.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1127 11.0 0 112.8 1.7364 332859.3\n",
      "we have 2 non-opposing shorted wedges for tract no 1129\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1131 2 271627.00646618207 2.4541\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1131 2 115287.79373641926 1.227\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1131 2 270594.9323085969 2.4475\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1131 2 220783.7142141857 1.8373\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1131 2 125898.27315850198 1.5322\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1131 2 136030.14069297534 1.6847\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1131 2 200913.42265618418 1.761\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1131 2 166716.18620079334 1.7229\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1131 2 184833.47861960312 1.7419\n",
      "I am working on tract number 1140 of 6896 tracts\n",
      "I am working on tract number 1160 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1178 3 480751.86957585387 1.8854\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1178 3 27275.103794724273 0.9427\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1178 3 480338.94551848073 1.8576\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1178 3 351301.394258834 1.4002\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1178 3 92200.50158614278 1.1714\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1178 3 177390.8059562379 1.2858\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1178 3 251018.73494546599 1.343\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1178 3 214382.98368662407 1.3144\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1178 3 196447.10828537485 1.3001\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1178 3 186843.5680249537 1.2929\n",
      "I am working on tract number 1180 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1184 3 602168.7961095255 2.1952\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1184 3 69065.21095895895 1.0976\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1184 3 534309.5960742377 1.9123\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1184 3 451266.3312121959 1.505\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1184 3 377500.41009554424 1.3013\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1184 3 118495.52389505098 1.1994\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1184 3 259335.04106735764 1.2504\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1184 3 182794.69684184395 1.2249\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1184 3 220627.88620339945 1.2376\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1184 3 201746.26457172033 1.2313\n",
      "we have 2 non-opposing shorted wedges for tract no 1198\n",
      "I am working on tract number 1200 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1201 3 583562.0583956996 2.2163\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1201 3 88820.17378433119 1.1082\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1201 3 519639.13815881446 1.907\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1201 3 435899.55659394956 1.5076\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1201 3 367763.42277647264 1.3079\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1201 3 159338.9097872129 1.208\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1201 3 295834.6517845195 1.2579\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1201 3 224946.28227805553 1.233\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1218 1 349190.51641086186 1.4493\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1218 1 34744.34787389159 0.7247\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1218 1 351742.6231617632 1.4594\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1218 1 174013.27828806386 1.092\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1218 1 320894.6427839331 1.2757\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1218 1 273739.3190999281 1.1839\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1218 1 238079.49272357314 1.138\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1218 1 211743.8747408294 1.115\n",
      "I am working on tract number 1220 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1228 2 266937.988774882 1.4211\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1228 2 14828.337074687559 0.7105\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1228 2 504369.63224031136 1.537\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1228 2 61916.05673489568 1.1237\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1228 2 107743.14099473006 1.3304\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1228 2 308406.2500768436 1.4337\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1228 2 145630.39174896837 1.382\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1228 2 224513.17345075388 1.4078\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1228 2 181911.2865154962 1.3949\n",
      "15 yoyos for tract,wedge,wedgePop,r= 1228 2 203330.44929108795 1.4014\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1231 0 339729.45326761017 4.3178\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1231 0 339711.74930218124 2.7337\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1231 0 291704.6159702599 1.9417\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1231 0 286841.08281145204 1.5457\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1231 0 258653.8189992102 1.3477\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1231 0 48839.78049411063 1.2487\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1231 0 139030.8634476922 1.2982\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1231 0 211109.26897556393 1.323\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1231 0 178641.79623883273 1.3106\n",
      "I am working on tract number 1240 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1249 9.0 0 93.6 0.6314 173176.4\n",
      "we have 2 non-opposing shorted wedges for tract no 1254\n",
      "we have 2 non-opposing shorted wedges for tract no 1255\n",
      "we have 2 non-opposing shorted wedges for tract no 1256\n",
      "I am working on tract number 1260 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1279 2 317728.91444886284 5.465\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1279 2 12289.70847093995 2.7325\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1279 2 336499.526850761 5.6433\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1279 2 69998.65348465391 4.1879\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1279 2 71589.68209347644 4.9156\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1279 2 143231.03513424983 5.2795\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1279 2 316291.90255242924 5.4614\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1279 2 241383.1112047563 5.3704\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1279 2 180994.11505513696 5.325\n",
      "I am working on tract number 1280 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1280 0 475413.1608568587 3.2715\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1280 0 120228.07581302081 1.6357\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1280 0 482650.43450591114 3.3602\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1280 0 156399.7655806769 2.498\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1280 0 184804.44357110414 2.9291\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1280 0 391298.08494756656 3.1446\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1280 0 211973.46375320863 3.0368\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1280 0 281773.7355320194 3.0907\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1280 0 240975.6668080069 3.0638\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1280 0 224082.83191354654 3.0503\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1286 12.0 0 88.6 0.6044 200490.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1286 13.0 0 88.6 0.5844 200490.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1286 14.0 0 88.6 0.5651 200490.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1286 15.0 0 88.6 0.5464 200490.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1286 0 200478.02363688577 0.5343\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1286 0 126427.37273297385 0.2672\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1286 0 200444.96189737966 0.5274\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1286 0 146618.64145240534 0.3973\n",
      "loop31.0, tr1286,wedgePops757046.19, 181296.8, 192016.2, 192140.2, 191593.0, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?10110 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0651, 191739.2,-0.0159, 191739.2,-0.0006, 191739.2,0.0004\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1286 0 181296.78755800036 0.4623\n",
      "loop32.0, tr1286,wedgePops775921.31, 200171.9, 192016.2, 192140.2, 191593.0, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?10110 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0325, 191739.2,-0.0159, 191739.2,-0.0006, 191739.2,0.0004\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1286 0 200171.9126927819 0.4949\n",
      "loop33.0, tr1286,wedgePops775344.98, 199595.6, 192016.2, 192140.2, 191593.0, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?11110 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,-0.0163, 191739.2,-0.0159, 191739.2,-0.0006, 191739.2,0.0004\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1286 0 199595.58554071008 0.4786\n",
      "loop34.0, tr1286,wedgePops769875.47, 194126.1, 192016.2, 192140.2, 191593.0, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?11110 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,-0.0081, 191739.2,-0.0159, 191739.2,-0.0006, 191739.2,0.0004\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1291 10.0 0 92.4 0.6067 177564.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1293 12.0 0 89.9 0.6506 192396.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1295 9.0 0 99.8 0.6542 143945.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1299 0 348139.72264593106 2.7853\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1299 0 18535.383079227002 1.3926\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1299 0 352221.7815096996 2.9191\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1299 0 312686.3411492324 2.1559\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1299 0 36947.413051679265 1.7742\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1299 0 87641.94083697452 1.9651\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1299 0 296628.6030156396 2.0605\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1299 0 206454.63265077217 2.0128\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1299 0 138689.42807175207 1.9889\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1299 0 173194.14727438573 2.0008\n",
      "I am working on tract number 1300 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1300 11.0 1 90.0 3.0266 379865.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1300 15.0 1 25.5 1.9676 555578.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1301 1 317337.35144092917 1.8625\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1301 1 15564.017136554816 0.9312\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1301 1 425548.9715682239 2.6779\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1301 1 244860.93711754886 1.8046\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1301 1 21563.797229670803 1.3679\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1301 1 31732.126564897888 1.5862\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1301 1 59263.75829165783 1.6954\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1301 1 143514.93252325262 1.75\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1302 3 513287.11491148267 1.307\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1302 1 946339.013836512 5.0821\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1302 3 43452.608176066016 0.6535\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1302 15.0 1 90.0 2.541 946339.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1302 1 946339.013836512 2.541\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1304 1 1001075.9003601685 3.6026\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1304 1 891711.0737495747 1.8013\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1304 1 19902.450410541496 0.9007\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1304 1 412393.56579892867 1.6368\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1304 1 25758.438190591813 1.2687\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1304 1 49908.764216430296 1.4528\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1304 1 164852.55583219975 1.5448\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1304 1 268796.66671410855 1.5908\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1304 1 208334.71239879366 1.5678\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1304 1 185879.35190420234 1.5563\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1304 1 196891.6471352274 1.562\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1306 0 372512.080037047 1.6682\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1306 0 62186.89796395949 0.8341\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1306 0 371478.9296215539 1.6619\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1306 0 229291.92336031425 1.248\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1306 0 71271.37700302355 1.0411\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1306 0 79695.40266084184 1.1445\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1306 0 126345.35823055795 1.1963\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1306 0 176109.40318443175 1.2221\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1306 0 203880.3291654582 1.2351\n",
      "I am working on tract number 1320 of 6896 tracts\n",
      "I am working on tract number 1340 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1353\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1354 6.0 1 39.5 1.7624 264945.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1354 7.0 1 39.5 1.6783 264945.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1357 0 214208.81352460387 1.8973\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1357 0 100632.90283762245 0.9486\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1357 0 214210.82985986292 1.8978\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1357 0 132706.78391659725 1.4232\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1357 0 160269.89189144748 1.6605\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1357 0 205117.799060822 1.7791\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1357 0 179354.26295229135 1.7198\n",
      "I am working on tract number 1360 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1360 5.0 3 62.6 1.6033 242044.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1366 3 774916.9956627595 1.8518\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1366 3 38416.09540996479 0.9259\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1366 3 542889.9815720469 1.6217\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1366 3 460901.46235617483 1.2738\n",
      "I am working on tract number 1380 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1381\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1394 1 449109.7793800995 0.8145\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1394 1 40325.362254626176 0.4073\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1394 1 455379.53447264916 0.8978\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1394 1 369971.6031800458 0.6525\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1394 1 84722.57839957206 0.5299\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1394 1 219220.2901814007 0.5912\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1394 1 140292.6987042434 0.5605\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1394 1 181197.74077310093 0.5759\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1394 1 201165.53599531174 0.5835\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1398 3 416449.42377352656 0.708\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1398 3 20140.930438685347 0.354\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1398 3 435708.9815788121 0.8197\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1398 3 248379.80448993176 0.5869\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1398 3 34240.42168631425 0.4704\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1398 3 92619.68061075678 0.5286\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1398 3 162561.14063584578 0.5578\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1398 3 204208.37964275765 0.5723\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1398 3 182213.1664093389 0.565\n",
      "I am working on tract number 1400 of 6896 tracts\n",
      "I am working on tract number 1420 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1425 8.0 1 90.0 5.7179 213964.0\n",
      "I am working on tract number 1440 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1442\n",
      "I am working on tract number 1460 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1472\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1474 3 271722.60948981484 2.2237\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1474 3 162124.10268583015 1.1119\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1474 3 448687.1906006845 2.4519\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1474 3 181945.2507024387 1.7819\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1474 3 190182.55789308308 2.1169\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1474 3 340121.80661446345 2.2844\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1474 3 235343.1756874491 2.2007\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1475 3 436216.40391655086 2.3827\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1475 3 148572.62150701182 1.1913\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1475 3 437986.4253645368 2.5652\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1475 3 169481.7203643865 1.8783\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1475 3 411509.2413595612 2.2217\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1475 3 193826.5468934091 2.05\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1475 3 288820.7422191156 2.1359\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1475 3 248977.42284826288 2.0929\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1475 3 215889.64726261055 2.0715\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1476 3 293298.03076849104 2.2314\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1476 3 167860.54448347862 1.1157\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1476 3 290397.59430396126 2.2271\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1476 3 187684.0505191578 1.6714\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1476 3 193429.3223042877 1.9493\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1476 3 198990.50918466525 2.0882\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1476 3 216757.81240810174 2.1576\n",
      "I am working on tract number 1480 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1483 3 439999.1565939883 2.3256\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1483 3 161943.50495507295 1.1628\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1483 3 440686.12037620234 2.7343\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1483 3 429478.50856702216 1.9485\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1483 3 175090.1010751209 1.5557\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1483 3 182350.70709410845 1.7521\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1483 3 280478.51991258253 1.8503\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1483 3 379982.39568426367 1.8994\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1483 3 316407.5392459945 1.8749\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1483 3 296192.74297638534 1.8626\n",
      "I am working on tract number 1500 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1502 1 1292286.6985566465 5.0399\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1502 1 549955.183751601 2.5199\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1502 1 55382.01882479747 1.26\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1502 1 482043.70630431816 2.0504\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1502 1 92133.2123024594 1.6552\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1502 1 416149.1866210911 1.8528\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1502 1 223792.67819421523 1.754\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1502 1 108506.49435362885 1.7046\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1502 1 154491.4174732525 1.7293\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1502 1 184528.14098494122 1.7416\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1502 1 203908.25010896326 1.7478\n",
      "I am working on tract number 1520 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1532 3 298243.2889889549 1.859\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1532 3 20772.195659574994 0.9295\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1532 3 298349.19693776587 2.0037\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1532 3 34139.23461810121 1.4666\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1532 3 286008.5433469841 1.7352\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1532 3 78525.36784592012 1.6009\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1532 3 182273.91046636802 1.668\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1532 3 261338.79971094473 1.7016\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1532 3 226362.07046705205 1.6848\n",
      "15 yoyos for tract,wedge,wedgePop,r= 1532 3 246903.5769901594 1.6932\n",
      "15 yoyos for tract,wedge,wedgePop,r= 1532 3 237433.13132997416 1.689\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1539 3 400540.39520147373 3.9928\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1539 3 302733.4117415603 1.9964\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1539 3 23587.365689385217 0.9982\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1539 3 295824.9155886235 1.8942\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1539 3 37472.24080250933 1.4462\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1539 3 45565.21694881636 1.6702\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1539 3 151043.94967540124 1.7822\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1539 3 260767.99693863635 1.8382\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1539 3 197247.03185886407 1.8102\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1539 3 232061.84612110577 1.8242\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1539 3 214352.07422392422 1.8172\n",
      "loop31.0, tr1539,wedgePops772208.56, 113970.1, 217525.2, 217269.7, 223443.5, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?01014 \n",
      "   targetWP, latest drx4 are tWP,dr, 217662.2,0.7191, 217662.2,0.0004, 217662.2,0.002, 217662.2,0.0035\n",
      "15 yoyos for tract,wedge,wedgePop,r= 1539 3 223443.511892298 1.8207\n",
      "loop32.0, tr1539,wedgePops767697.21, 113970.1, 217525.2, 217269.7, 218932.2, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?01015 \n",
      "   targetWP, latest drx4 are tWP,dr, 217662.2,0.7191, 217662.2,0.0004, 217662.2,0.002, 217662.2,-0.0017\n",
      "I am working on tract number 1540 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1542 3 585147.9454488569 5.2437\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1542 3 452847.49848298787 3.2477\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1542 3 433892.39731553063 2.2498\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1542 3 168874.856357845 1.7508\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1542 3 330158.4492994661 2.0003\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1542 3 180514.17842924668 1.8755\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1542 3 242019.48819075432 1.9379\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1542 3 194513.63151308175 1.9067\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1546 3 360403.42422020336 1.9794\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1546 3 84453.34663747315 0.9897\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1546 3 364755.0867934536 2.6534\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1546 3 207845.8475276583 1.8216\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1546 3 363732.7903889556 2.2375\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1546 3 363438.26275399094 2.0295\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1546 3 355083.4801070407 1.9256\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1546 3 312246.5793416508 1.8736\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1546 3 247706.50137429993 1.8476\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1546 3 224154.44438293937 1.8346\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1546 3 234609.42952664578 1.8411\n",
      "loop31.0, tr1546,wedgePops765861.43, 78605.9, 228962.8, 229261.7, 229031.0, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?00013 \n",
      "   targetWP, latest drx4 are tWP,dr, 229450.3,0.712, 229450.3,0.0012, 229450.3,0.0006, 229450.3,-0.0032\n",
      "we have 2 non-opposing shorted wedges for tract no 1552\n",
      "I am working on tract number 1560 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1565 3 401751.8990111308 1.7612\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1565 3 127430.90941147902 0.8806\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1565 3 511759.97271571466 1.9163\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1565 3 146245.9628850394 1.3984\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1565 3 239473.4081881081 1.6574\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1565 3 151303.9862442176 1.5279\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1565 3 161065.3534422737 1.5926\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1565 3 187593.74758539654 1.625\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1565 3 208834.27584902177 1.6412\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1565 3 197283.00392237975 1.6331\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1569 2 334658.8767504984 2.0196\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1569 2 179125.66506033388 1.0098\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1569 2 324771.01241147274 2.0023\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1569 2 182415.12034686623 1.5061\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1569 2 190021.02454023104 1.7542\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1572 3 468143.3993554576 3.9952\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1572 3 372474.575257538 1.9976\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1572 3 99193.43707932165 0.9988\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1572 3 113661.498810008 1.5306\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1572 3 128657.67573685222 1.7966\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1572 3 321932.26352951233 1.9295\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1572 3 207729.0076853874 1.863\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1572 3 247119.4751125301 1.8963\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1572 3 282456.43366875395 1.9129\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1572 3 303209.19568157505 1.9212\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1572 3 293000.8460917904 1.9171\n",
      "loop31.0, tr1572,wedgePops766861.8, 116706.4, 75350.1, 287147.3, 287658.0, Overedge?1, 1, 0, 0, ,Satisfied?0010,yoyo?00011 \n",
      "   targetWP, latest drx4 are tWP,dr, 287450.2,0.972, 287450.2,0.189, 287450.2,0.0009, 287450.2,-0.0021\n",
      "we have 2 non-opposing shorted wedges for tract no 1575\n",
      "I am working on tract number 1580 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1590 0 382019.339247126 2.8883\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1590 0 105385.75415006961 1.4441\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1590 0 385344.17608978756 3.1658\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1590 0 375331.083249136 2.305\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1590 0 156942.28637323494 1.8746\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1590 0 374458.0037928544 2.0898\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1590 0 356123.97722151317 1.9822\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1590 0 269181.19064593257 1.9284\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1590 0 216227.87772946322 1.9015\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1590 0 183359.27739888383 1.888\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1590 0 199782.7655075683 1.8947\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 2.0 0 90.0 0.4102 303012.0\n",
      "I am working on tract number 1600 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1604 2.0 0 90.0 0.3956 280478.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1604 3 388540.1963962964 2.5202\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1604 3 110078.98748570704 1.2601\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1604 3 395763.8957857978 2.9827\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1604 3 387560.82641893823 2.1214\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1604 3 123489.43719993159 1.6907\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1604 3 175727.43232552975 1.9061\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1604 3 371585.9020568514 2.0137\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1604 3 289933.3716522666 1.9599\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1604 3 237125.72897312243 1.933\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1604 3 204561.95505247702 1.9195\n",
      "I am working on tract number 1620 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1625 1 1058207.7150565046 3.9071\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1625 1 117285.47229450848 1.9535\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1625 1 656479.5196945684 3.5569\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1625 1 411021.52080382017 2.7552\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1625 1 129326.53993831197 2.3544\n",
      "loop31.0, tr1625,wedgePops771897.14, 157967.0, 207930.1, 203313.6, 202686.4, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01012 \n",
      "   targetWP, latest drx4 are tWP,dr, 202996.6,1.2776, 202996.6,0.2004, 202996.6,-0.001, 202996.6,0.0019\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1625 1 207930.10040749476 2.5548\n",
      "loop32.0, tr1625,wedgePops704109.98, 157967.0, 140142.9, 203313.6, 202686.4, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01112 \n",
      "   targetWP, latest drx4 are tWP,dr, 202996.6,1.2776, 202996.6,-0.1002, 202996.6,-0.001, 202996.6,0.0019\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1625 1 140142.93895506475 2.4546\n",
      "loop33.0, tr1625,wedgePops719514.63, 157967.0, 155547.6, 203313.6, 202686.4, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01212 \n",
      "   targetWP, latest drx4 are tWP,dr, 202996.6,1.2776, 202996.6,0.0501, 202996.6,-0.001, 202996.6,0.0019\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1625 1 155547.5891743095 2.5047\n",
      "loop34.0, tr1625,wedgePops743683.14, 157967.0, 179716.1, 203313.6, 202686.4, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01212 \n",
      "   targetWP, latest drx4 are tWP,dr, 202996.6,1.2776, 202996.6,0.0251, 202996.6,-0.001, 202996.6,0.0019\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1625 1 179716.09999891365 2.5298\n",
      "loop35.0, tr1625,wedgePops757471.09, 157967.0, 193504.1, 203313.6, 202686.4, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01212 \n",
      "   targetWP, latest drx4 are tWP,dr, 202996.6,1.2776, 202996.6,0.0125, 202996.6,-0.001, 202996.6,0.0019\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1625 1 193504.05083815026 2.5423\n",
      "loop36.0, tr1625,wedgePops764981.4, 157967.0, 201014.4, 203313.6, 202686.4, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01212 \n",
      "   targetWP, latest drx4 are tWP,dr, 202996.6,1.2776, 202996.6,0.0063, 202996.6,-0.001, 202996.6,0.0019\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1626 2 1612452.5205725299 7.0941\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1626 2 347317.79241696675 3.5471\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1626 2 35218.82843331713 1.7735\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1626 2 309989.16787334747 2.9918\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1626 2 261425.1711055427 2.3827\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1626 2 36615.4556081185 2.0781\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1626 2 104712.30250856656 2.2304\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1626 2 179877.75987570145 2.3065\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1626 2 230178.74801152255 2.3446\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1626 2 209634.90890956915 2.3256\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1627 21.0 0 101.2 7.6017 231137.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1634 2 540684.1643251817 2.9854\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1634 2 38798.24644074432 1.4927\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1634 2 510160.82406701375 2.8549\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1634 2 374142.67011892697 2.1738\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1634 2 72670.19891432289 1.8333\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1634 2 261403.8115367 2.0035\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1634 2 113658.38457040826 1.9184\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1634 2 187111.3544916953 1.961\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1634 2 226025.8158676918 1.9822\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1634 2 207526.0820728957 1.9716\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1634 2 197289.4708269689 1.9663\n",
      "I am working on tract number 1640 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1649 3 92078.35356427642 0.9753\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1649 3 663268.2282405682 4.0829\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1649 3 598206.7011515622 2.5291\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1649 3 470736.90780639567 1.7522\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1649 3 368492.20081073267 1.3637\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1649 3 148307.35750756154 1.1695\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1649 3 265437.0675157476 1.2666\n",
      "I am working on tract number 1660 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1678 0 208529.144214996 1.8243\n",
      "I am working on tract number 1680 of 6896 tracts\n",
      "I am working on tract number 1700 of 6896 tracts\n",
      "I am working on tract number 1720 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1721 2.0 1 90.0 1.1163 274459.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1724 2 490867.0751514387 2.499\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1724 2 128789.49634238193 1.2495\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1724 2 489210.3967456026 2.4929\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1724 2 177692.7512579948 1.8712\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1724 2 310760.09413561446 2.1821\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1724 2 198604.50476785252 2.0266\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1724 2 218057.37470573795 2.1044\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1724 2 254906.7632120849 2.1432\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1724 2 278980.7598468637 2.1626\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1724 2 293868.00196566264 2.1724\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1726 3 1637172.2359962028 2.4707\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1726 3 238736.06008982984 1.2353\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1726 3 1650797.8563319698 2.5671\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1726 3 1507604.2412834377 1.9012\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1726 3 1058672.2164697647 1.5683\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1726 3 767503.4385214443 1.4018\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1726 3 508249.6621411242 1.3186\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1729 3 1631343.7056709798 3.0594\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1729 3 1005356.6822148004 1.5297\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1729 3 13251.557844198425 0.7649\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1729 3 824403.0900644988 1.4274\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1729 3 27088.092886290862 1.0961\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1729 3 352070.1987900552 1.2618\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1729 3 125522.78651778796 1.179\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1729 3 229051.38764305037 1.2204\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1729 3 282625.08575135097 1.2411\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1729 3 315556.8430161125 1.2514\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1729 3 333056.9549160699 1.2566\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1738 0 484519.8257152452 2.7015\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1738 0 82824.71407986572 1.3508\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1738 0 488364.89404470613 2.7646\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1738 0 107696.19042893598 2.0577\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1738 0 368728.604040022 2.4112\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1738 0 120852.65909249813 2.2344\n",
      "I am working on tract number 1740 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1741 6.0 0 136.9 0.9678 198982.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1741 3 232784.771544475 2.1137\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1741 3 96476.91709588285 1.0569\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1741 3 233187.5503287591 2.1215\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1741 3 118889.42966903951 1.5892\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1741 3 150869.72928120688 1.8553\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1741 3 223302.58831119473 1.9884\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1741 3 214114.71080889876 1.9218\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1741 3 185042.46225641132 1.8886\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1741 3 205187.7409762819 1.9052\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1745 7.0 1 90.0 4.7912 200923.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1745 8.0 1 90.0 4.625 200923.5\n",
      "I am working on tract number 1760 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1761 2 1227698.9063958921 7.7325\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1761 15.0 2 90.0 4.4788 1227698.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1761 2 1227698.9063958921 4.4788\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1761 16.0 2 90.0 2.8519 1227698.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1761 2 1227698.9063958921 2.8519\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1761 2 922679.8560043457 2.0384\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1761 2 33326.783924137824 1.6317\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1761 2 377423.7054983847 1.8351\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1761 2 109186.3620293457 1.7334\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1761 2 223790.0558405667 1.7842\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1761 2 157800.1057611314 1.7588\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1779 3 513261.6546187376 9.2565\n",
      "I am working on tract number 1780 of 6896 tracts\n",
      "I am working on tract number 1800 of 6896 tracts\n",
      "I am working on tract number 1820 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1826 3 1086209.089018883 6.6408\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1826 16.0 3 90.0 3.8891 1086209.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1826 3 1086209.089018883 3.8891\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1826 17.0 3 90.0 2.5133 1086209.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1826 3 1086209.089018883 2.5133\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1826 3 521933.39687139634 1.8254\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1826 3 73704.33609671256 1.4814\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1826 3 139987.77705582688 1.6534\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1826 3 294422.6889764938 1.7394\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1826 3 205883.2292904656 1.6964\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1826 3 168301.15409562475 1.6749\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1826 3 186103.43823797378 1.6857\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1828 0 435900.01548304444 3.5901\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1828 0 74623.71486282349 1.7951\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1828 0 428234.88858546235 3.2667\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1828 0 166939.26050310372 2.5309\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1828 0 394614.25858375704 2.8988\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1828 0 389605.15911160264 2.7148\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1828 0 366932.7150501553 2.6229\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1828 0 284359.43473865016 2.5769\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1828 0 224161.85595854907 2.5539\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1833 12.0 3 90.0 2.2207 602043.2\n",
      "I am working on tract number 1840 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1844 3 216205.66182171073 3.14\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1844 3 80420.54051776 1.57\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1844 3 299625.18845807563 3.3339\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1844 3 129640.49576000238 2.4519\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1844 3 146255.5612463927 2.8929\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1844 3 197260.40524908196 3.1134\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1844 3 287935.41682893626 3.2236\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1844 3 250931.07000759852 3.1685\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1844 3 217036.62848454952 3.1409\n",
      "I am working on tract number 1860 of 6896 tracts\n",
      "I am working on tract number 1880 of 6896 tracts\n",
      "I am working on tract number 1900 of 6896 tracts\n",
      "I am working on tract number 1920 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1923 11.0 0 85.8 7.7693 223079.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1938 3 676674.9587208233 14.6252\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1938 3 575405.3608169117 7.3126\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1938 3 126500.59995325445 3.6563\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1938 3 517919.85228761745 5.9144\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1938 3 134428.66558336752 4.7854\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1938 3 310528.0261993491 5.3499\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1938 3 141607.17745276744 5.0676\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1938 3 172234.2677590606 5.2087\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1938 3 261469.67939559018 5.2793\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1938 3 208453.40265764864 5.244\n",
      "15 yoyos for tract,wedge,wedgePop,r= 1938 3 234747.27487408364 5.2617\n",
      "15 yoyos for tract,wedge,wedgePop,r= 1938 3 220322.34781574048 5.2529\n",
      "loop31.0, tr1938,wedgePops766792.69, 124437.9, 214398.9, 213925.8, 214030.0, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?01215 \n",
      "   targetWP, latest drx4 are tWP,dr, 214173.0,1.2776, 214173.0,-0.001, 214173.0,0.0189, 214173.0,-0.0044\n",
      "I am working on tract number 1940 of 6896 tracts\n",
      "I am working on tract number 1960 of 6896 tracts\n",
      "I am working on tract number 1980 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1987 0 226941.83620372514 2.5479\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1987 0 133052.88612775103 1.2739\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1987 0 225399.7325318138 2.4726\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1987 0 156005.28933767433 1.8733\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1987 0 171548.295613307 2.1729\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1987 0 179429.69801360212 2.3228\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1987 0 208985.98094918847 2.3977\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1987 0 185065.8290954863 2.3602\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1987 0 197124.53423729373 2.379\n",
      "I am working on tract number 2000 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2005\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2006 2 450366.1514153386 3.0933\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2006 2 93473.8690217653 1.5466\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2006 3 343568.9394867451 3.7508\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2006 2 475707.33588781214 3.6257\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2006 3 142100.52512703318 1.8754\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2006 2 307304.9985341955 2.5862\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2006 3 345021.0911414407 3.8107\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2006 2 111069.11808479828 2.0664\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2006 3 196011.5645492306 2.8431\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2006 2 127519.97655075189 2.3263\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2006 3 206935.31043738304 3.3269\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2006 2 180337.69875146728 2.4562\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2006 3 329424.2222060981 3.5688\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2006 2 246368.755330856 2.5212\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2006 3 230226.2643670239 3.4478\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2018 3 964849.9555689137 7.3594\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2018 15.0 3 90.0 4.2736 964850.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2018 3 964849.9555689137 4.2736\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2018 16.0 3 90.0 2.7307 964850.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2018 3 964849.9555689137 2.7307\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2018 3 945469.238388623 1.9593\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2018 3 63305.89029337757 1.5736\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2018 3 429679.04102011723 1.7664\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2018 3 198998.1472621253 1.67\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2018 3 119575.97681178965 1.6218\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2018 3 154555.0720719606 1.6459\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2018 3 173701.88672123745 1.6579\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2018 3 185829.69532898563 1.664\n",
      "I am working on tract number 2020 of 6896 tracts\n",
      "I am working on tract number 2040 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2055 2 193811.6260328267 3.0303\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2055 3 328904.9042043616 1.8642\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2055 2 34485.37844530202 1.5152\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2055 3 17714.952320872806 0.9321\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2055 2 201232.2075280515 3.0534\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2055 3 328976.58566054446 1.8831\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2055 2 85101.01593672998 2.2843\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2055 3 68125.8556225478 1.4076\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2055 2 91046.04333129874 2.6689\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2055 3 303542.48734986887 1.6453\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2055 2 97004.6590863168 2.8612\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2055 3 152510.61231588188 1.5265\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2055 2 127650.80461471161 2.9573\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2055 3 246191.65346591477 1.5859\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2055 2 170757.0003173267 3.0054\n",
      "14 yoyos for tract,wedge,wedgePop,r= 2055 3 190128.95266501833 1.5562\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2055 2 193119.0816627003 3.0294\n",
      "15 yoyos for tract,wedge,wedgePop,r= 2055 3 217085.24615224786 1.571\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2056 1 304060.4189334427 2.4367\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2056 1 3870.4824442000827 1.2184\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2056 1 280466.39444758114 2.1028\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2056 1 269579.75518772064 1.6606\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2056 1 120950.72768565372 1.4395\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2056 1 249231.61896902704 1.55\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2056 1 186827.41428680962 1.4948\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2056 1 220005.8543130431 1.5224\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2057 12.0 2 90.0 8.7453 249319.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2058 1 197556.49147076075 1.6358\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2058 1 58536.501839567616 0.8179\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2058 1 286691.769268023 1.7138\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2058 1 62217.12622890374 1.2659\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2058 1 68003.88091206954 1.4898\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2058 1 152455.30851839643 1.6018\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2058 1 237564.31495087376 1.6578\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2058 1 185831.23080638796 1.6298\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2058 1 214403.65200854593 1.6438\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2058 1 199705.93691309783 1.6368\n",
      "I am working on tract number 2060 of 6896 tracts\n",
      "I am working on tract number 2080 of 6896 tracts\n",
      "I am working on tract number 2100 of 6896 tracts\n",
      "I am working on tract number 2120 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2122\n",
      "I am working on tract number 2140 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2145\n",
      "we have 2 non-opposing shorted wedges for tract no 2148\n",
      "we have 2 non-opposing shorted wedges for tract no 2149\n",
      "we have 2 non-opposing shorted wedges for tract no 2156\n",
      "I am working on tract number 2160 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2170 3 331166.47687375674 0.5447\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2170 3 24396.231758057664 0.2723\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2170 3 376867.3535873323 0.749\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2170 3 222832.07470894285 0.5107\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2170 3 31791.392136211303 0.3915\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2170 3 37128.085360961384 0.4511\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2170 3 92721.68324004428 0.4809\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2170 3 160682.78897212382 0.4958\n",
      "I am working on tract number 2180 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2180 3.0 0 83.3 0.8675 264430.0\n",
      "we have 2 non-opposing shorted wedges for tract no 2184\n",
      "I am working on tract number 2200 of 6896 tracts\n",
      "I am working on tract number 2220 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2220 0 460970.78007696324 2.9298\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2220 0 78140.48150469433 1.4649\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2220 0 469312.07951424597 3.0849\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2220 0 133445.18921995827 2.2749\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2220 0 445508.4996862539 2.6799\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2220 0 405821.15670738736 2.4774\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2220 0 240946.85345273028 2.3761\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2220 0 161569.1720347313 2.3255\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2220 0 200390.09498500367 2.3508\n",
      "loop31.0, tr2220,wedgePops754595.93, 179038.2, 191534.7, 192136.5, 191886.5, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?13131 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,-0.0127, 191739.2,-0.5569, 191739.2,-0.0043, 191739.2,-0.0393\n",
      "14 yoyos for tract,wedge,wedgePop,r= 2220 0 179038.21630089925 2.3382\n",
      "loop32.0, tr2220,wedgePops765262.32, 189704.6, 191534.7, 192136.5, 191886.5, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?14131 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0063, 191739.2,-0.5569, 191739.2,-0.0043, 191739.2,-0.0393\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2228 3.0 3 55.9 0.7356 435609.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2229 3.0 3 36.3 1.1642 436773.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2229 4.0 3 36.3 0.9619 436773.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2232 1 6409.387827377417 0.9238\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2232 1 807892.3616247454 3.4173\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2232 1 562178.1860832849 2.1706\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2232 1 62726.428708356165 1.5472\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2232 1 469548.81297262746 1.8589\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2232 1 440765.44439180853 1.7031\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2232 1 210331.8483179246 1.6251\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2232 1 101984.24406852204 1.5862\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2232 1 154570.54409298478 1.6057\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2232 1 182809.2999977635 1.6154\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2232 1 195856.72675272412 1.6203\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2233 3 404920.21256387443 2.5967\n",
      "I am working on tract number 2240 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2245 3 424781.9452930583 2.2945\n",
      "we have 2 non-opposing shorted wedges for tract no 2246\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2247 1 699366.9387384844 2.9039\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2247 1 76460.20195707132 1.4519\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2247 1 763893.933981165 3.4978\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2247 1 576481.2784307683 2.4749\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2247 1 106097.76885423058 1.9634\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2247 1 289847.1236051022 2.2192\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2247 1 477459.8196557734 2.347\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2247 1 394919.07452967664 2.2831\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2247 1 347537.768190895 2.2511\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2247 1 374055.7780983898 2.2671\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2247 1 361478.2629734736 2.2591\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2250 1 707986.4615315625 2.8959\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2250 1 81026.86700244888 1.4479\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2250 1 750902.49280777 3.2489\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2250 1 552838.2493990646 2.3484\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2250 1 109676.56425752467 1.8982\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2250 1 239000.80842792284 2.1233\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2250 1 433811.8466174812 2.2359\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2250 1 350803.1865166858 2.1796\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2250 1 395322.07583502174 2.2077\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2250 1 375523.2545290665 2.1937\n",
      "14 yoyos for tract,wedge,wedgePop,r= 2250 1 363815.44821901014 2.1866\n",
      "we have 2 non-opposing shorted wedges for tract no 2253\n",
      "I am working on tract number 2260 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2261 0 204688.01644068537 1.5545\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2261 0 14327.581580068945 0.7773\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2261 0 496724.5358610556 1.753\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2261 0 54014.523933944176 1.2651\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2261 3 287072.6240676092 2.53\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2261 0 113400.16548485507 1.509\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2261 3 71538.94876912844 1.265\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2261 0 396303.9877544222 1.631\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2261 3 287244.40405540646 2.5337\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2261 0 240482.1362486872 1.57\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2261 3 113738.87215263105 1.8993\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2261 0 168317.31357451284 1.5395\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2261 3 261366.49710929647 2.2165\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2261 0 205232.807381285 1.5548\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2261 3 167303.76704154693 2.0579\n",
      "14 yoyos for tract,wedge,wedgePop,r= 2261 0 187372.5165504957 1.5471\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2261 3 239988.39477929223 2.1372\n",
      "15 yoyos for tract,wedge,wedgePop,r= 2261 0 196619.0406604957 1.551\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2261 3 212949.46016950643 2.0976\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2262 0 397269.5543587525 1.5428\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2262 0 22974.52140103723 0.7714\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2262 0 479204.4081849326 1.7103\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2262 0 56037.09166964318 1.2409\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2262 0 205957.44425419235 1.4756\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2262 0 64788.765069486224 1.3582\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2262 0 84701.3226102656 1.4169\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2262 0 132707.6736661713 1.4462\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2262 0 170121.95637327514 1.4609\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2262 0 187538.58523441237 1.4682\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2262 0 196571.33418273536 1.4719\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2270 1 285770.8409388182 4.2395\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2270 1 32094.85006018894 2.1197\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2270 1 274812.70131775807 4.2175\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2270 1 73942.25055711417 3.1686\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2270 1 112779.96732470507 3.693\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2270 1 142482.94894884742 3.9553\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2270 1 235326.94658582663 4.0864\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2270 1 160587.9905466031 4.0208\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2270 1 184216.51080828364 4.0536\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2270 1 208474.24307572946 4.07\n",
      "I am working on tract number 2280 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2285 3 266805.4754847996 1.6906\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2285 3 34514.411847003415 0.8453\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2285 3 264019.508430511 1.4537\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2285 3 258201.2093769873 1.1495\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2285 3 123391.36192438088 0.9974\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2285 3 231490.69749234326 1.0735\n",
      "we have 2 non-opposing shorted wedges for tract no 2294\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2298 3 449883.7216859124 2.6872\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2298 3 38232.18502477434 1.3436\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2298 3 453846.83571115293 2.6945\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2298 3 378823.7309739435 2.0191\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2298 3 93168.91025028419 1.6813\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2298 3 340083.12163372093 1.8502\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2298 3 215624.28642125876 1.7658\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2298 3 148515.5193796291 1.7236\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2298 3 179936.62010131436 1.7447\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2298 3 196681.82364687475 1.7552\n",
      "we have 2 non-opposing shorted wedges for tract no 2299\n",
      "I am working on tract number 2300 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2308 3 367881.4599973797 2.702\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2308 3 47664.51659569837 1.351\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2308 3 368263.0186783731 2.7102\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2308 3 347673.5772651377 2.0306\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2308 3 69502.29215580947 1.6908\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2308 3 103156.07198461298 1.8607\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2308 3 274783.1925454461 1.9456\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2308 3 153333.0806332967 1.9032\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2308 3 214526.57669881222 1.9244\n",
      "14 yoyos for tract,wedge,wedgePop,r= 2308 3 181969.48267811205 1.9138\n",
      "15 yoyos for tract,wedge,wedgePop,r= 2308 3 197182.54725042116 1.9191\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2309 10.0 2 90.0 2.6807 459691.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2312 3 466741.62631682586 3.4213\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2313 0 448374.96950853965 2.9656\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2313 0 57714.07196896017 1.4828\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2313 0 448354.77838548756 2.91\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2313 0 344411.85068347014 2.1964\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2313 0 69761.62094662891 1.8396\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2313 0 134443.24144583513 2.018\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2313 0 315398.68348972284 2.1072\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2313 0 220899.2022429186 2.0626\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2313 0 164077.72627990978 2.0403\n",
      "I am working on tract number 2320 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2335\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2337 3.0 1 40.2 1.1641 245838.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2337 4.0 1 40.2 0.9601 245838.0\n",
      "we have 2 non-opposing shorted wedges for tract no 2338\n",
      "I am working on tract number 2340 of 6896 tracts\n",
      "I am working on tract number 2360 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2369 0 327309.7238157993 1.8835\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2372 6.0 1 90.0 1.5695 224307.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2372 11.0 1 90.0 1.875 224307.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2372 12.0 1 90.0 1.6629 224307.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2372 17.0 1 90.0 1.7673 224307.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2372 18.0 1 90.0 1.5673 224307.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2372 1 224307.70653419377 2.1983\n",
      "loop31.0, tr2372,wedgePops759856.87, 161853.1, 194911.9, 201517.7, 201574.2, Overedge?1, 0, 0, 0, ,Satisfied?0010,yoyo?0200 \n",
      "   targetWP, latest drx4 are tWP,dr, 201701.2,0.5859, 201701.2,0.0238, 201701.2,0.0009, 201701.2,0.0007\n",
      "loop32.0, tr2372,wedgePops768775.58, 161853.1, 203830.6, 201517.7, 201574.2, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0200 \n",
      "   targetWP, latest drx4 are tWP,dr, 201701.2,0.5859, 201701.2,0.0191, 201701.2,0.0009, 201701.2,0.0007\n",
      "I am working on tract number 2380 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2382 9.0 0 91.1 0.6428 185540.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2384 11.0 0 93.4 0.6118 172919.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2397 3.0 2 90.0 0.7597 130808.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2398 1 467442.71453957265 2.1753\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2398 1 96050.17152616888 1.0877\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2398 1 467432.00275137235 2.1734\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2398 1 431773.6667722888 1.6305\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2398 1 271403.907810036 1.3591\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2398 1 110594.59882319928 1.2234\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2398 1 182354.41802875296 1.2912\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2398 1 242965.2655554039 1.3252\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2398 1 211213.44929164872 1.3082\n",
      "I am working on tract number 2400 of 6896 tracts\n",
      "I am working on tract number 2420 of 6896 tracts\n",
      "I am working on tract number 2440 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2459 0 198438.5265895626 1.9792\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2459 0 119987.7465008175 0.9896\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2459 0 279337.2860128471 2.0183\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2459 0 139269.96032028916 1.5039\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2459 0 155181.44697184346 1.7611\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2459 0 164492.17501817242 1.8897\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2459 0 178762.49943339592 1.954\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2459 0 211440.93874750138 1.9861\n",
      "I am working on tract number 2460 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2461 16.0 3 90.0 8.8908 228636.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2461 17.0 3 90.0 7.7674 228636.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2461 18.0 3 90.0 6.7859 228636.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2462 0 426454.2281899224 2.6183\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2462 0 163374.2748048706 1.3092\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2462 0 432311.86459068704 2.751\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2462 0 377476.0771734463 2.0301\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2462 0 201948.75970978092 1.6696\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2462 0 165071.14027205855 1.4894\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2462 0 167473.71689234817 1.5795\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2462 0 172727.4193712595 1.6246\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2462 0 181756.72387126158 1.6471\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2464 3 634142.1591108497 2.4655\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2464 3 29514.122920278693 1.2328\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2464 3 640791.0811700792 2.5074\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2464 3 477510.5382077185 1.8701\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2464 3 87960.22385684308 1.5514\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2464 3 141084.29288123958 1.7108\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2464 3 292564.3517723309 1.7904\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2464 3 181342.88703054673 1.7506\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2464 3 224287.64291217836 1.7705\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2466 2.0 2 90.0 1.3462 173293.5\n",
      "I am working on tract number 2480 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2482 2 258448.70510449383 2.7275\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2482 2 114346.4554105872 1.3637\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2482 2 258304.18729859404 2.7259\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2482 1 237557.46568456883 1.0462\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2482 2 165546.79409748325 2.0448\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2482 1 219812.05856300556 0.5231\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2482 2 197364.87913360252 2.3853\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2482 1 237548.22465050596 1.0456\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2482 2 232051.12688958048 2.5556\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2482 1 233797.726320323 0.7844\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2482 2 203664.46211576555 2.4705\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2482 1 221014.42424468388 0.6537\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2482 2 207201.16498518782 2.513\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2482 1 224818.25804829015 0.7191\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2482 2 217384.68878045396 2.5343\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2483 2 433358.9634581607 2.8694\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2483 2 43805.48159429862 1.4347\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2483 2 435806.38198563625 2.9194\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2483 2 297721.49220258475 2.177\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2483 2 70980.16025151417 1.8059\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2483 2 88669.28912947624 1.9914\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2483 2 137582.66804669483 2.0842\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2483 2 220581.8025277335 2.1306\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2483 2 179655.83212924196 2.1074\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2483 2 199883.10375320024 2.119\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2485 0 293952.79756977456 3.1883\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2485 0 148498.44878570692 1.5941\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2485 0 294009.81419061596 3.1897\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2485 0 271041.5886117136 2.3919\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2485 0 157331.26410142425 1.993\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2485 0 184720.49716735695 2.1925\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2485 0 258691.1080245137 2.2922\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2485 0 235369.1100523842 2.2423\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2485 0 211211.11612767386 2.2174\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2485 0 197234.480371381 2.2049\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2492 2 439855.3058121511 2.7933\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2492 2 36287.09722100553 1.3966\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2492 2 472902.44369616767 2.9879\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2492 2 368249.1412379598 2.1922\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2492 2 62692.81795812666 1.7944\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2492 0 236578.25209468315 3.652\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2492 2 101160.89401565603 1.9933\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2495 2 515509.8429866364 2.4325\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2495 2 132340.9207423618 1.2162\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2495 2 529446.2684323719 2.5014\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2495 2 432430.25330609776 1.8588\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2495 2 142730.84861319093 1.5375\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2495 2 243584.54010711316 1.6982\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2495 2 161782.82114531193 1.6178\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2496 3 321515.848700046 3.6031\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2496 3 131664.5203375575 1.8016\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2496 3 322501.76890706806 3.6238\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2496 3 265143.4335616667 2.7127\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2496 3 156251.66909549656 2.2571\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2496 3 256283.84232223377 2.4849\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2496 3 242150.54876641088 2.371\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2496 3 214924.94402020785 2.3141\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2496 3 184838.38928809267 2.2856\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2496 3 201216.5471216102 2.2998\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2497 3 454048.2939921272 3.0988\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2497 3 45644.64485398389 1.5494\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2497 3 453581.39813849074 3.0975\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2497 19.0 0 90.0 6.769 255448.6\n",
      "loop31.0, tr2497,wedgePops767879.59, 191647.3, 194598.0, 189736.2, 191898.1, Overedge?0, 0, 0, 0, ,Satisfied?1000,yoyo?0333 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0617, 191739.2,-0.0318, 191739.2,-0.0523, 191739.2,-0.0005\n",
      "I am working on tract number 2500 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2516 1 202980.76698968667 2.6981\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2516 1 105436.74970502757 1.349\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2516 1 214195.98295441037 2.8516\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2516 1 145436.17080376705 2.1003\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2516 1 157514.40425750337 2.476\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2516 1 177263.8934828932 2.6638\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2516 1 210258.07927991103 2.7577\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2516 1 207570.8256526483 2.7108\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2516 1 196292.83378809073 2.6873\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2516 1 186805.00946573113 2.6755\n",
      "I am working on tract number 2520 of 6896 tracts\n",
      "I am working on tract number 2540 of 6896 tracts\n",
      "I am working on tract number 2560 of 6896 tracts\n",
      "I am working on tract number 2580 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2586\n",
      "we have 2 non-opposing shorted wedges for tract no 2588\n",
      "I am working on tract number 2600 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2602 3 865986.938703008 3.9936\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2602 3 750649.3133191585 2.1046\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2602 3 91123.73323748668 1.1601\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2602 3 617058.1534202769 1.6324\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2602 3 456330.09923040995 1.3963\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2602 3 151453.20825207693 1.2782\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2602 3 300649.84251842205 1.3372\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2602 3 364458.9679255613 1.3668\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2602 3 329429.7532087682 1.352\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2602 3 315334.03714646515 1.3446\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2604 0 198211.53569510017 1.7319\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2604 0 44906.51209981434 0.8659\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2604 0 226385.38934434045 1.8134\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2604 0 57888.17097600864 1.3397\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2604 0 62997.57792671476 1.5765\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2604 0 157503.6331955518 1.6949\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2604 0 211544.72264056708 1.7541\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2605 2 247578.55370288712 2.4678\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2605 2 120342.78213920546 1.2339\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2605 2 247587.3403953691 2.4679\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2605 2 157971.52769703494 1.8509\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2605 2 188628.08967478955 2.1594\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2605 2 222825.17452281096 2.3136\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2605 2 194520.65087476742 2.2365\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2605 2 197986.23355789453 2.275\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2605 2 208853.18284413297 2.2943\n",
      "we have 2 non-opposing shorted wedges for tract no 2619\n",
      "I am working on tract number 2620 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2627 0 362438.413923791 2.9866\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2627 0 76827.13302873977 1.4933\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2627 0 361610.78736552026 2.975\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2627 0 255098.4250830342 2.2342\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2627 0 94127.92560550713 1.8637\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2627 0 118585.38764735943 2.049\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2627 0 165838.42411532116 2.1416\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2627 0 207054.91482562368 2.1879\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2627 0 184792.10302257133 2.1647\n",
      "I am working on tract number 2640 of 6896 tracts\n",
      "I am working on tract number 2660 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2669 0 198513.11781540362 2.0138\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2669 0 48392.31501611398 1.0069\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2669 0 391176.907268347 2.1535\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2669 0 90214.93775335007 1.5802\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2669 0 108237.76281340109 1.8669\n",
      "I am working on tract number 2680 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2680 0 239473.92268252396 1.5439\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2680 0 43070.943124344834 0.772\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2680 3 574175.9249151745 2.1438\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2680 0 239410.77085109692 1.5399\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2680 3 91333.03345169552 1.0719\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2680 0 99170.09691079764 1.1559\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2680 3 517443.69990888884 1.915\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2680 0 207434.79711602058 1.3479\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2680 3 422915.65979525365 1.4934\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2680 0 148727.40436619852 1.2519\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2680 3 337272.68971159274 1.2827\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2680 0 188695.11822639947 1.2999\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2680 3 117980.31110592486 1.1773\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2680 0 199551.62643582432 1.3239\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2680 3 205590.6836352752 1.23\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2680 0 194194.51012335886 1.3119\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2680 3 145493.73522277706 1.2036\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2680 3 173839.79941651155 1.2168\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2688 3 606410.359335712 3.444\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2688 3 26337.031418561353 1.722\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2688 3 669107.4470692569 3.6041\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2688 3 444532.7504171174 2.6631\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2688 3 67788.32901380025 2.1925\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2688 3 337370.8120408594 2.4278\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2688 3 100875.16185665879 2.3102\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2688 3 197664.496408 2.369\n",
      "14 yoyos for tract,wedge,wedgePop,r= 2688 3 138186.03074439027 2.3396\n",
      "14 yoyos for tract,wedge,wedgePop,r= 2688 3 165969.25908370348 2.3543\n",
      "14 yoyos for tract,wedge,wedgePop,r= 2688 3 180962.99779320718 2.3616\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2690 12.0 0 90.0 1.6069 202601.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2697 4.0 0 139.7 1.8407 295678.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2697 5.0 0 139.7 1.6467 295678.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2698 3 264657.33977453486 0.8281\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2698 3 943.2914542974904 0.4141\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2698 3 264713.9574997764 0.8889\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2698 3 136988.4391369552 0.6515\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2698 3 258876.72844795813 0.7702\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2698 3 229283.54628320874 0.7108\n",
      "I am working on tract number 2700 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2700 3 506494.5733737601 11.8041\n",
      "I am working on tract number 2720 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2728\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2733 3 231381.25918949256 1.433\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2733 3 3570.089683610451 0.7165\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2733 3 271076.87367706513 1.4819\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2733 3 15870.63270709262 1.0992\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2733 3 28420.027168430162 1.2905\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2733 3 134157.30818639533 1.3862\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2733 3 233210.82617612515 1.434\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2733 3 175674.80655442513 1.4101\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2733 3 206619.01943903006 1.4221\n",
      "I am working on tract number 2740 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2758 1 217523.87631168298 4.0948\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2758 1 33009.17409247247 2.0474\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2758 1 217610.2692257037 4.0974\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2758 1 122410.60715972836 3.0724\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2758 1 148584.57572227635 3.5849\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2758 1 204606.183624777 3.8411\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2758 1 157736.55924688847 3.713\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2758 1 172851.74930386632 3.7771\n",
      "I am working on tract number 2760 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2763 4.0 0 90.0 1.819 198503.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2763 9.0 0 90.0 1.8523 198503.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2763 10.0 0 90.0 1.8046 198503.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2763 15.0 0 90.0 1.8621 198503.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2763 16.0 0 90.0 1.8141 198503.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2763 0 198503.88176314163 1.8849\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2776 3 386026.30654992897 3.9771\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2776 3 308884.76698937814 1.9886\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2776 3 30446.850871256378 0.9943\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2776 3 44306.785082120914 1.5141\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2776 3 186359.73480388106 1.774\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2776 3 305162.652268563 1.904\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2776 3 291743.7090709792 1.839\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2776 3 263738.3343270763 1.8065\n",
      "I am working on tract number 2780 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2792 1 261800.696767175 2.7603\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2792 1 103553.78965857037 1.3802\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2792 1 257937.79043042363 2.6441\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2792 1 215657.21572666525 2.0121\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2792 1 114561.44947509955 1.6962\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2792 1 127298.297334097 1.8541\n",
      "I am working on tract number 2800 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2814\n",
      "we have 2 non-opposing shorted wedges for tract no 2816\n",
      "we have 2 non-opposing shorted wedges for tract no 2818\n",
      "we have 2 non-opposing shorted wedges for tract no 2819\n",
      "I am working on tract number 2820 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2821\n",
      "we have 2 non-opposing shorted wedges for tract no 2824\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2827 2.0 3 90.0 1.0787 299078.2\n",
      "I am working on tract number 2840 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2845\n",
      "I am working on tract number 2860 of 6896 tracts\n",
      "I am working on tract number 2880 of 6896 tracts\n",
      "I am working on tract number 2900 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2904 1 272366.46078717103 1.8696\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2904 1 117637.22513052507 0.9348\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2904 1 272422.0409395856 1.871\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2904 1 153466.39056949335 1.4029\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2904 1 262044.02923912412 1.637\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2904 1 228807.02747575843 1.5199\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2919 0 310478.3442234114 2.5781\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2919 0 121636.9622054219 1.289\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2919 0 310015.7578090455 2.5752\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2919 0 265001.4418918694 1.9321\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2919 0 129783.09933866284 1.6106\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2919 0 185887.34040754594 1.7713\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2919 0 238453.44837525173 1.8517\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2919 0 212036.7683658771 1.8115\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2919 0 199980.6933547296 1.7914\n",
      "I am working on tract number 2920 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2920 3 212287.30959379737 1.3807\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2920 3 96617.5857938281 0.6903\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2920 3 219114.46808056708 1.4747\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2920 3 111338.01307379048 1.0825\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2920 3 162426.951483202 1.2786\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2920 3 211990.49119889498 1.3766\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2920 3 203665.5646681874 1.3276\n",
      "I am working on tract number 2940 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2945 1 209230.10396079416 2.7851\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2945 1 82507.26743669675 1.3925\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2945 1 312748.280176982 2.8376\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2945 1 109316.50244834603 2.1151\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2945 1 138382.61360294427 2.4764\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2945 1 159278.5547333213 2.657\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2945 1 178916.92487946418 2.7473\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2945 1 220745.68262013767 2.7925\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2953 3.0 1 90.0 2.5992 195952.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2953 4.0 1 90.0 2.557 195952.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2953 5.0 1 90.0 2.5156 195952.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2953 6.0 1 90.0 2.4748 195952.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2953 7.0 1 90.0 2.4347 195952.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2953 2 613420.3171859833 2.6355\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2953 2 23496.301605267916 1.3177\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2953 2 634336.8649529604 2.7018\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2953 2 462360.8308785191 2.0098\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2953 2 209284.83646495116 1.6638\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2953 2 49900.62990575048 1.4907\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2953 2 74227.64515243773 1.5773\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2953 2 126469.75115809073 1.6205\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2953 2 167418.46467286756 1.6421\n",
      "I am working on tract number 2960 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2966 1 311863.2894344841 2.7824\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2966 1 116194.20935347959 1.3912\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2966 1 311925.96589667443 2.787\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2966 1 148781.4361988408 2.0891\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2966 1 294693.9106728112 2.438\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2966 1 253862.62849721138 2.2636\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2966 1 170851.0377708132 2.1763\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2966 1 219576.82653607597 2.2199\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2966 1 237818.23139355608 2.2418\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2970 6.0 3 41.1 3.0584 222873.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2978 1 237423.75266199428 2.3184\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2978 1 103392.62060268241 1.1592\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2978 1 267386.2856111475 2.4247\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2978 1 129401.85942952897 1.792\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2978 1 139585.12041262904 2.1083\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2978 1 191264.71039335968 2.2665\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2978 1 246566.11149099228 2.3456\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2978 1 228211.13983348722 2.3061\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2978 1 240158.2895528837 2.3258\n",
      "I am working on tract number 2980 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2991 1 201113.95990043742 2.1838\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2991 1 47455.8343242445 1.0919\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2991 1 269258.84769735776 2.2702\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2991 1 88068.2375455354 1.681\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2991 1 113939.56506439333 1.9756\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2991 1 150314.59288989898 2.1229\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2991 1 214670.34645361936 2.1965\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2991 1 175813.3028798265 2.1597\n",
      "I am working on tract number 3000 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3000 2 7686925.269629941 5.2506\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3000 2 945509.1335372413 2.6253\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3000 2 59448.15603506274 1.3126\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3000 2 846014.8029276333 2.1111\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3000 2 134031.53688739822 1.7119\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3000 2 487443.0639762945 1.9115\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3000 2 270597.8256764964 1.8117\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3000 2 180528.68631149427 1.7618\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3000 2 217609.42525601693 1.7867\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3000 2 197329.3450784676 1.7742\n",
      "14 yoyos for tract,wedge,wedgePop,r= 3000 2 188704.9413907528 1.768\n",
      "I am working on tract number 3020 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3031 3 312912.08089490375 2.2658\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3033 1 212568.40675859846 4.1789\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3033 3 457561.6477869683 2.9421\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3033 1 100153.87162139098 2.0894\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3033 3 73494.02525467717 1.471\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3033 1 215123.62414069788 4.4831\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3033 3 458860.24204033986 2.9675\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3033 1 157419.53867560916 3.2863\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3033 3 121042.70014848799 2.2193\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3033 1 178599.43505969635 3.8847\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3033 3 435662.1679580856 2.5934\n",
      "loop31.0, tr3033,wedgePops976462.94, 170818.2, 212613.8, 198486.9, 394544.1, Overedge?1, 0, 0, 0, ,Satisfied?0010,yoyo?010211 \n",
      "   targetWP, latest drx4 are tWP,dr, 198712.9,1.2776, 198712.9,0.2992, 198712.9,0.0074, 198712.9,-0.1871\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3033 1 212613.80501096539 4.1839\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3033 3 394544.0981147795 2.4063\n",
      "loop32.0, tr3033,wedgePops829433.39, 170818.2, 211255.0, 198486.9, 248873.4, Overedge?1, 0, 0, 0, ,Satisfied?0010,yoyo?011211 \n",
      "   targetWP, latest drx4 are tWP,dr, 198712.9,1.2776, 198712.9,-0.1496, 198712.9,0.0074, 198712.9,-0.0935\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3033 1 211254.96313194197 4.0343\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3033 3 248873.38638135788 2.3128\n",
      "loop33.0, tr3033,wedgePops747835.7, 170818.2, 208106.6, 198486.9, 170424.0, Overedge?1, 0, 0, 0, ,Satisfied?0010,yoyo?011211 \n",
      "   targetWP, latest drx4 are tWP,dr, 198712.9,1.2776, 198712.9,-0.0748, 198712.9,0.0074, 198712.9,-0.0468\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3033 1 208106.6341735217 3.9595\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3033 3 170424.0315799605 2.266\n",
      "loop34.0, tr3033,wedgePops778687.22, 170818.2, 197018.8, 198486.9, 212363.3, Overedge?1, 0, 0, 0, ,Satisfied?0010,yoyo?011212 \n",
      "   targetWP, latest drx4 are tWP,dr, 198712.9,1.2776, 198712.9,-0.0374, 198712.9,0.0074, 198712.9,0.0234\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3033 1 197018.82930950177 3.9221\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3033 3 212363.34975838102 2.2894\n",
      "loop35.0, tr3033,wedgePops764883.39, 170818.2, 203325.3, 198486.9, 192253.0, Overedge?1, 0, 0, 0, ,Satisfied?0010,yoyo?012213 \n",
      "   targetWP, latest drx4 are tWP,dr, 198712.9,1.2776, 198712.9,0.0187, 198712.9,0.0074, 198712.9,-0.0117\n",
      "we have 2 non-opposing shorted wedges for tract no 3035\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3036 3 456901.4675493853 3.0297\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3036 3 62457.89884349081 1.5149\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3036 3 456455.332384351 3.0204\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3036 3 311733.82061195053 2.2676\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3036 3 83514.81520567613 1.8912\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3036 3 94264.8068288869 2.0794\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3036 3 143943.6059135799 2.1735\n",
      "loop31.0, tr3036,wedgePops793190.3, 155754.0, 203527.1, 203608.5, 230300.7, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?01010 \n",
      "   targetWP, latest drx4 are tWP,dr, 203734.3,1.2776, 203734.3,-0.1226, 203734.3,0.0008, 203734.3,0.047\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3036 3 230300.72761686664 2.2206\n",
      "loop32.0, tr3036,wedgePops750727.08, 155754.0, 203527.1, 203608.5, 187837.5, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?01011 \n",
      "   targetWP, latest drx4 are tWP,dr, 203734.3,1.2776, 203734.3,-0.1226, 203734.3,0.0008, 203734.3,-0.0235\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3036 3 187837.5120885537 2.197\n",
      "loop33.0, tr3036,wedgePops771781.99, 155754.0, 203527.1, 203608.5, 208892.4, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?01012 \n",
      "   targetWP, latest drx4 are tWP,dr, 203734.3,1.2776, 203734.3,-0.1226, 203734.3,0.0008, 203734.3,0.0118\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3036 3 208892.4166894185 2.2088\n",
      "loop34.0, tr3036,wedgePops761081.81, 155754.0, 203527.1, 203608.5, 198192.2, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?01013 \n",
      "   targetWP, latest drx4 are tWP,dr, 203734.3,1.2776, 203734.3,-0.1226, 203734.3,0.0008, 203734.3,-0.0059\n",
      "14 yoyos for tract,wedge,wedgePop,r= 3036 3 198192.2342501812 2.2029\n",
      "loop35.0, tr3036,wedgePops766298.15, 155754.0, 203527.1, 203608.5, 203408.6, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?01014 \n",
      "   targetWP, latest drx4 are tWP,dr, 203734.3,1.2776, 203734.3,-0.1226, 203734.3,0.0008, 203734.3,0.0029\n",
      "I am working on tract number 3040 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3043 15.0 1 -272.4 6.2254 214413.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3043 16.0 1 -272.4 5.7178 214413.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3045 3 1257993.994411599 4.4704\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3045 3 1241239.7282323132 2.2352\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3045 3 26650.616952232085 1.1176\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3045 3 1181121.8791892612 1.9929\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3046 1 437137.38029159565 4.4994\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3046 1 55598.75990732032 2.2497\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3046 1 428552.2160991698 4.4349\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3046 1 155712.99512382713 3.3423\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3046 1 395117.17993999115 3.8886\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3046 1 377899.7965618482 3.6154\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3046 1 363849.3838986704 3.4789\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3046 1 297118.01856374077 3.4106\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3046 1 200946.18476637814 3.3764\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3046 1 251372.39734030515 3.3935\n",
      "14 yoyos for tract,wedge,wedgePop,r= 3046 1 220670.64334222826 3.385\n",
      "loop31.0, tr3046,wedgePops762453.44, 241026.9, 236508.6, 240692.6, 44225.3, Overedge?0, 0, 0, 1, ,Satisfied?1010,yoyo?31400 \n",
      "   targetWP, latest drx4 are tWP,dr, 240910.5,-0.003, 240910.5,0.0043, 240910.5,0.0116, 240910.5,1.2776\n",
      "14 yoyos for tract,wedge,wedgePop,r= 3046 1 236508.56279712328 3.3892\n",
      "loop32.0, tr3046,wedgePops770055.9, 241026.9, 244111.0, 240692.6, 44225.3, Overedge?0, 0, 0, 1, ,Satisfied?1010,yoyo?31400 \n",
      "   targetWP, latest drx4 are tWP,dr, 240910.5,-0.003, 240910.5,0.0021, 240910.5,0.0116, 240910.5,1.2776\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3047 0 1325901.1089699375 3.6539\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3047 0 1029315.5269371653 1.8269\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3047 0 19136.298508794862 0.9135\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3047 0 443917.7115956876 1.6145\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3047 0 24998.599956176535 1.264\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3047 0 69134.65966324799 1.4393\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3047 0 234480.61467044047 1.5269\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3047 0 132387.78737504024 1.4831\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3047 0 178839.39626513983 1.505\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3047 0 206550.15941080204 1.5159\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3049 2 527388.2848679989 1.3913\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3049 2 52956.69861117704 0.6957\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3049 2 539541.4815451407 1.437\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3049 2 461887.61337256205 1.0664\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3049 2 77581.54574186367 0.881\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3049 2 247987.94495129463 0.9737\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3049 2 126594.56517534486 0.9273\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3049 2 185067.6970730398 0.9505\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3049 2 216739.5243572881 0.9621\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3049 2 200850.7453095651 0.9563\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3053 0 461722.9417670807 8.3604\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3053 23.0 0 141.9 5.2553 461722.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3053 0 461722.9417670807 5.2553\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3053 0 305630.04156675923 3.7028\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3053 0 295326.4400938133 2.9265\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3053 0 210694.84233340132 2.5384\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3053 0 91036.48764636934 2.3443\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3053 0 134763.64569873892 2.4413\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3053 0 168086.47696012474 2.4898\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3053 0 188942.5608544413 2.5141\n",
      "loop31.0, tr3053,wedgePops769821.48, 199654.6, 195771.7, 195604.4, 178790.7, Overedge?0, 0, 0, 1, ,Satisfied?0110,yoyo?8000 \n",
      "   targetWP, latest drx4 are tWP,dr, 196055.4,0.0121, 196055.4,0.0955, 196055.4,0.0093, 196055.4,1.2776\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3054 4.0 0 90.0 0.5518 147624.6\n",
      "I am working on tract number 3060 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3063\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3069 4.0 1 90.0 1.5389 223905.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3070 3 273915.3616144124 3.1588\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3070 3 112534.9025434884 1.5794\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3070 3 326956.3143599159 3.1762\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3070 3 124892.38973973575 2.3778\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3070 3 150731.14561124326 2.777\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3070 3 193515.80252635884 2.9766\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3070 3 225562.6425752524 3.0764\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3070 3 237082.075235183 3.1263\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3070 3 258471.39821150186 3.1513\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3070 3 244103.73733271894 3.1388\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3070 3 249632.21936750412 3.145\n",
      "I am working on tract number 3080 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3095 3 391111.2369140161 1.705\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3095 3 43816.50585114403 0.8525\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3095 3 391118.21160337626 1.705\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3095 3 97885.7134612907 1.2788\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3095 3 322161.96558473713 1.4919\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3095 3 179802.74464237838 1.3853\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3095 3 284440.4853960605 1.4386\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3095 3 234567.06745081354 1.412\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3095 3 207981.88386184798 1.3987\n",
      "I am working on tract number 3100 of 6896 tracts\n",
      "I am working on tract number 3120 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3126 2 239950.24915093323 1.9903\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3126 2 106159.69231180058 0.9952\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3126 2 240097.1698229877 1.9972\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3126 2 149414.32833377013 1.4962\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3126 2 230884.39943129697 1.7467\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3126 2 216341.4196311694 1.6214\n",
      "I am working on tract number 3140 of 6896 tracts\n",
      "I am working on tract number 3160 of 6896 tracts\n",
      "I am working on tract number 3180 of 6896 tracts\n",
      "I am working on tract number 3200 of 6896 tracts\n",
      "I am working on tract number 3220 of 6896 tracts\n",
      "I am working on tract number 3240 of 6896 tracts\n",
      "I am working on tract number 3260 of 6896 tracts\n",
      "I am working on tract number 3280 of 6896 tracts\n",
      "I am working on tract number 3300 of 6896 tracts\n",
      "I am working on tract number 3320 of 6896 tracts\n",
      "I am working on tract number 3340 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3350 2 502689.32206097955 9.0038\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3350 2 439459.0860340957 5.0468\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3350 2 140262.11270878813 3.0684\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3350 2 399047.0818089271 4.0576\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3350 2 144484.8787810907 3.563\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3350 2 160896.7709308755 3.8103\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3350 2 288439.68654329324 3.934\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3350 2 222026.49112634984 3.8721\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3350 2 188146.1239766727 3.8412\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3350 2 204395.2214318087 3.8567\n",
      "I am working on tract number 3360 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3366 3 396132.4222709411 3.2568\n",
      "loop31.0, tr3366,wedgePops986141.12, 410247.2, 192046.6, 192019.7, 191827.6, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6310 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,1.4427, 191739.2,-0.0033, 191739.2,-0.0003, 191739.2,0.0448\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3366 0 410247.2278561152 3.2955\n",
      "loop32.0, tr3366,wedgePops628709.86, 52816.0, 192046.6, 192019.7, 191827.6, Overedge?1, 0, 0, 0, ,Satisfied?0111,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 238047.0,-1.6477, 238047.0,-0.0033, 238047.0,-0.0003, 238047.0,0.0448\n",
      "loop33.0, tr3366,wedgePops752390.53, 52816.0, 232873.5, 233305.4, 233395.6, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0110 \n",
      "   targetWP, latest drx4 are tWP,dr, 238047.0,-1.6477, 238047.0,0.043, 238047.0,0.0125, 238047.0,0.5023\n",
      "loop34.0, tr3366,wedgePops760775.16, 52816.0, 236011.6, 236982.4, 234965.2, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0110 \n",
      "   targetWP, latest drx4 are tWP,dr, 238047.0,-1.6477, 238047.0,0.0043, 238047.0,0.0011, 238047.0,0.0407\n",
      "loop35.0, tr3366,wedgePops765477.94, 52816.0, 237479.8, 237826.6, 237355.5, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0110 \n",
      "   targetWP, latest drx4 are tWP,dr, 238047.0,-1.6477, 238047.0,0.0022, 238047.0,0.0003, 238047.0,0.0627\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3376 3 252645.5130034812 1.6921\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3376 3 129908.24487324785 0.846\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3376 3 260797.17682192515 1.7407\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3376 3 142812.53181367693 1.2934\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3376 3 165880.40433109726 1.517\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3376 3 238487.439442034 1.6288\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3376 3 200314.27141248662 1.5729\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3376 3 179914.73240490712 1.545\n",
      "I am working on tract number 3380 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3380 1 360217.6811615197 4.3869\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3380 1 162322.66966881166 2.1934\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3380 1 332816.84586247965 3.9266\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3380 1 216737.0171007788 3.06\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3380 1 323002.08892794844 3.4933\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3380 1 318356.68313068117 3.2767\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3380 1 293949.3592959939 3.1683\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3380 1 271037.3277977083 3.1142\n",
      "we have 2 non-opposing shorted wedges for tract no 3382\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3391 1 246168.77748067566 2.8337\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3391 1 60211.853646506876 1.4168\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3391 1 251975.77592274986 2.9763\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3391 1 99580.87911148352 2.1966\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3391 1 234831.1675340147 2.5864\n",
      "I am working on tract number 3400 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3406 5.0 3 90.0 0.5424 185363.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3406 6.0 3 90.0 0.5563 185363.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3406 7.0 3 90.0 0.5705 185363.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3406 8.0 3 90.0 0.5851 185363.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3406 9.0 3 90.0 0.6 185363.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3406 10.0 3 90.0 0.6154 185363.1\n",
      "I am working on tract number 3420 of 6896 tracts\n",
      "I am working on tract number 3440 of 6896 tracts\n",
      "I am working on tract number 3460 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3472 3 272006.96647361934 2.897\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3472 3 43742.11627792928 1.4485\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3472 3 277983.53028691304 2.977\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3472 3 75805.4411145064 2.2128\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3472 3 225863.2183717827 2.5949\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3472 3 101179.13438976319 2.4038\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3472 3 140502.98084514574 2.4994\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3472 3 184554.33591327316 2.5471\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3472 3 207076.9507612324 2.571\n",
      "I am working on tract number 3480 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3484 0 328685.6878153697 1.2081\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3484 0 67121.96601506189 0.6041\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3484 0 325824.72361050185 1.1738\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3484 0 177786.85087056994 0.8889\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3484 0 304726.82190568617 1.0314\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3484 0 260929.09633747235 0.9602\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3484 0 222793.05089574587 0.9246\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3484 0 202293.89153602123 0.9068\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3485 2 302888.1068568276 1.2106\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3485 2 75110.1945177032 0.6053\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3485 2 302747.50164906424 1.2086\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3485 2 196375.54082897486 0.907\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3485 2 91681.49257717053 0.7561\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3485 2 116032.42992588555 0.8315\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3485 2 155934.33493009344 0.8693\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3485 2 179318.00559595827 0.8881\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3485 2 187435.00075300943 0.8975\n",
      "I am working on tract number 3500 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3517 6.0 1 90.0 2.0432 317535.2\n",
      "I am working on tract number 3520 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3521\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3525 1 287016.8156367786 1.9127\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3525 1 25695.05952699686 0.9563\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3525 1 288441.10838407296 1.959\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3525 1 26434.470665972825 1.4577\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3525 1 216357.65836997377 1.7084\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3525 1 281657.5797663748 1.8337\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3525 1 269183.5076310756 1.771\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3525 1 245180.77035748627 1.7397\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3536 2 472974.48785584455 1.0456\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3536 2 37381.6685513549 0.5228\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3536 2 482712.96711024595 1.1088\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3536 2 436814.7312176499 0.8158\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3536 2 122486.46691243461 0.6693\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3536 2 306403.14390814974 0.7425\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3536 2 211702.2638106175 0.7059\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3536 2 162194.8518698793 0.6876\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3538 3 467712.05712394335 1.0281\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3538 3 73740.05183556036 0.514\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3538 3 449199.78020198754 0.9726\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3538 3 396457.6669431073 0.7433\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3538 3 200790.82906372112 0.6287\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3538 3 111644.48757596646 0.5714\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3538 3 151018.3815321721 0.6\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3538 3 176486.27371971446 0.6144\n",
      "I am working on tract number 3540 of 6896 tracts\n",
      "I am working on tract number 3560 of 6896 tracts\n",
      "I am working on tract number 3580 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3585 3 411437.56085393927 3.3512\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3585 3 36316.248101642646 1.6756\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3585 3 411417.9306025862 3.351\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3585 3 249517.80636146202 2.5133\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3585 3 67538.03378717898 2.0945\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3585 3 85532.54487867086 2.3039\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3585 3 96779.63115524557 2.4086\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3585 3 145837.75697541286 2.4609\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3585 3 199085.38688063176 2.4871\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3585 3 171550.96813865352 2.474\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3585 3 185420.59488228493 2.4806\n",
      "I am working on tract number 3600 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3615\n",
      "we have 2 non-opposing shorted wedges for tract no 3616\n",
      "we have 2 non-opposing shorted wedges for tract no 3618\n",
      "we have 2 non-opposing shorted wedges for tract no 3619\n",
      "I am working on tract number 3620 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3622\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3627 3 262025.15976483616 2.7743\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3627 3 74704.12432554548 1.3872\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3627 3 262007.53137412507 2.773\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3627 3 114410.76942312173 2.0801\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3627 3 244269.07206317352 2.4265\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3627 3 203864.5722288053 2.2533\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3627 3 158029.0364804176 2.1667\n",
      "I am working on tract number 3640 of 6896 tracts\n",
      "I am working on tract number 3660 of 6896 tracts\n",
      "I am working on tract number 3680 of 6896 tracts\n",
      "I am working on tract number 3700 of 6896 tracts\n",
      "I am working on tract number 3720 of 6896 tracts\n",
      "I am working on tract number 3740 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3751 2.0 1 90.0 1.2509 206724.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3751 3.0 1 90.0 1.1816 206724.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3754 3 241221.36115649733 0.8627\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3754 3 7635.008440594072 0.4313\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3754 3 266032.88906819536 0.8962\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3754 3 30735.723043216887 0.6638\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3754 3 79057.61414794106 0.78\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3754 3 196745.82328321296 0.8381\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3754 3 129565.45042028041 0.809\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3754 3 163421.38342318186 0.8236\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3754 3 180446.17053368664 0.8308\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3755 3 317523.4683390382 1.0991\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3755 3 17837.690159707447 0.5496\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3755 3 317622.404746277 1.0996\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3755 3 99632.15606047725 0.8246\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3755 3 289280.5615641648 0.9621\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3755 3 232157.41605904925 0.8933\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3755 3 161044.6799984682 0.859\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3757 3 352616.4050884922 1.4223\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3757 3 35774.74407536187 0.7112\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3757 3 352585.9620994891 1.4221\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3757 3 153250.65762138506 1.0667\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3757 3 325922.1043498938 1.2444\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3757 3 280278.13916525105 1.1555\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3757 3 228237.57559574003 1.1111\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3757 3 186904.74427325535 1.0889\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3757 3 207906.19417398638 1.1\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3757 3 196937.09419247636 1.0944\n",
      "I am working on tract number 3760 of 6896 tracts\n",
      "I am working on tract number 3780 of 6896 tracts\n",
      "I am working on tract number 3800 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3808 12.0 0 120.7 2.3293 206137.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3814 13.0 0 88.7 2.4437 196922.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3814 14.0 0 88.7 2.3951 196922.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3814 15.0 0 88.7 2.3475 196922.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3814 16.0 0 88.7 2.3009 196922.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3814 17.0 0 88.7 2.2551 196922.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3814 18.0 0 88.7 2.2103 196922.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3814 23.0 0 88.7 2.4452 196922.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3814 24.0 0 88.7 2.3966 196922.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3814 25.0 0 88.7 2.349 196922.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3814 26.0 0 88.7 2.3023 196922.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3814 27.0 0 88.7 2.2566 196922.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3814 28.0 0 88.7 2.2117 196922.8\n",
      "loop31.0, tr3814,wedgePops710209.81, 135838.3, 191266.6, 191600.2, 191504.7, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6222 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.8348, 191739.2,0.0019, 191739.2,0.0022, 191739.2,0.0028\n",
      "loop32.0, tr3814,wedgePops771294.34, 196922.8, 191266.6, 191600.2, 191504.7, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6222 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.5616, 191739.2,0.0019, 191739.2,0.0022, 191739.2,0.0028\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3814 0 196922.81239798278 2.4802\n",
      "loop33.0, tr3814,wedgePops678239.33, 103867.8, 191266.6, 191600.2, 191504.7, Overedge?1, 0, 0, 0, ,Satisfied?0111,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 221029.7,-1.2401, 221029.7,0.0019, 221029.7,0.0022, 221029.7,0.0028\n",
      "loop34.0, tr3814,wedgePops753779.79, 103867.8, 216980.5, 210459.1, 222472.4, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 221029.7,-1.2401, 221029.7,0.0282, 221029.7,0.0786, 221029.7,0.063\n",
      "loop35.0, tr3814,wedgePops766093.87, 103867.8, 220411.6, 220681.9, 221132.6, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0001 \n",
      "   targetWP, latest drx4 are tWP,dr, 221029.7,-1.2401, 221029.7,0.0035, 221029.7,0.0338, 221029.7,-0.0023\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3817 6.0 0 125.0 2.4719 193700.7\n",
      "I am working on tract number 3820 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3823\n",
      "we have 2 non-opposing shorted wedges for tract no 3824\n",
      "I am working on tract number 3840 of 6896 tracts\n",
      "I am working on tract number 3860 of 6896 tracts\n",
      "I am working on tract number 3880 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3885 1 525372.4084168756 1.1699\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3885 1 88481.69323641685 0.585\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3885 1 522187.9466491303 1.1398\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3885 1 455606.0001193124 0.8624\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3885 1 132412.39772141224 0.7237\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3885 1 305729.9504028624 0.793\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3885 1 202181.95131580072 0.7583\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3885 1 163382.45613634321 0.741\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3885 1 181478.71211698672 0.7497\n",
      "I am working on tract number 3900 of 6896 tracts\n",
      "I am working on tract number 3920 of 6896 tracts\n",
      "I am working on tract number 3940 of 6896 tracts\n",
      "I am working on tract number 3960 of 6896 tracts\n",
      "I am working on tract number 3980 of 6896 tracts\n",
      "I am working on tract number 4000 of 6896 tracts\n",
      "I am working on tract number 4020 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4027 9.0 0 100.4 0.6051 141200.3\n",
      "I am working on tract number 4040 of 6896 tracts\n",
      "I am working on tract number 4060 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4068 1 267233.98282236897 1.9132\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4068 1 30749.538891490927 0.9566\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4068 1 403600.3807921362 2.0509\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4068 1 74949.11012889887 1.5038\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4068 1 89364.78556982813 1.7773\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4068 1 269311.4311434309 1.9141\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4068 1 128045.79557430025 1.8457\n",
      "I am working on tract number 4080 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4084\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4086 3 15520.205472209549 0.2798\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4086 3 441723.5222838133 1.3841\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4086 3 399960.7633931332 0.8319\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4086 3 374866.5897159445 0.5558\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4086 3 36772.690669112955 0.4178\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4086 3 209176.67241382203 0.4868\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4086 3 318788.5177461279 0.5213\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4086 3 265190.5664316332 0.5041\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4086 3 237522.5429616878 0.4954\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4086 3 223414.45338575312 0.4911\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4088 5.0 1 90.0 2.6323 298160.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4089 3 212047.46810128918 0.4449\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4089 3 10499.708350455447 0.2224\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4089 3 243757.58863371218 0.4969\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4089 3 15156.204483725916 0.3597\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4089 3 169937.3402307085 0.4283\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4089 3 238447.74255306448 0.4626\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4089 3 213373.88807872098 0.4455\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4091 0 292109.6501122298 3.5171\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4091 0 59073.48765405736 1.7586\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4091 0 292102.5843889186 3.3016\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4091 0 237458.6638957596 2.5301\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4091 0 90830.71181671252 2.1443\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4091 0 142296.34711461063 2.3372\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4091 0 182280.15189307343 2.4336\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4091 0 204930.8444457741 2.4818\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4093 9.0 0 140.9 3.0805 239957.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4093 14.0 0 140.9 3.0768 239957.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4093 3 382343.2136890724 0.6629\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4093 19.0 0 140.9 3.1019 239957.6\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4093 3 39558.1291040692 0.3315\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4093 3 384914.01052798494 0.7375\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4093 3 331726.61699108133 0.5345\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4093 3 49459.44266530522 0.433\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4093 0 239957.57736874078 3.1415\n",
      "I am working on tract number 4100 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4103\n",
      "I am working on tract number 4120 of 6896 tracts\n",
      "I am working on tract number 4140 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4146\n",
      "we have 2 non-opposing shorted wedges for tract no 4147\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4155 1 259584.70092889768 0.56\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4155 1 438.8497998021485 0.28\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4155 1 262174.6968740688 0.5912\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4155 1 29599.977509076474 0.4356\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4155 1 214920.5234687821 0.5134\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4155 1 113497.67974338388 0.4745\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4155 1 165876.7154602008 0.4939\n",
      "I am working on tract number 4160 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4166 7.0 1 89.6 1.9576 207930.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4166 8.0 1 89.6 1.841 207930.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4166 13.0 1 89.6 2.0912 207930.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4166 14.0 1 89.6 1.982 207930.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4166 15.0 1 89.6 1.8785 207930.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4166 16.0 1 89.6 1.7804 207930.1\n",
      "I am working on tract number 4180 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4188 1 257704.48029581844 1.5488\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4188 1 162700.02603689698 0.7744\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4188 1 398479.1851918384 1.6109\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4188 1 201075.7122686133 1.1927\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4188 1 217109.76248190325 1.4018\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4188 1 229368.35312902802 1.5064\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4188 3 415959.139284953 2.6223\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4188 1 268842.05729363736 1.5586\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4188 3 13763.519271878991 1.3111\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4188 1 240948.83842266342 1.5325\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4188 3 500958.074985535 3.3736\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4188 1 254175.9874494981 1.5456\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4188 3 68537.8196057048 2.3424\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4188 1 261339.01291058803 1.5521\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4188 3 461644.1041413863 2.858\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4188 1 257743.28018173008 1.5488\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4188 3 349933.4361126796 2.6002\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4188 1 255951.67507735832 1.5472\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4188 3 95365.2669223689 2.4713\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4188 3 109129.53612415084 2.5358\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4188 3 224209.29276920942 2.568\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4188 3 294872.7556890093 2.5841\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4188 3 262628.6331295924 2.576\n",
      "14 yoyos for tract,wedge,wedgePop,r= 4188 3 243888.50219654597 2.572\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4192 0 292317.9851949353 3.0736\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4192 0 146056.37548122776 1.5368\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4192 0 290128.62940184923 2.7944\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4192 0 183384.8588041341 2.1656\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4192 0 283853.8659620243 2.48\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4192 0 257050.63973109625 2.3228\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4192 0 236477.4514525758 2.2442\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4192 0 221944.3746901548 2.2049\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4192 0 202828.57235863231 2.1853\n",
      "I am working on tract number 4200 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4210\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4211 2 516088.0767215175 4.5832\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4211 2 121792.21925442718 2.2916\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4211 2 483205.46518264234 3.9572\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4211 2 450255.7814902817 3.1244\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4212 2 649369.3850096156 5.3084\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4212 2 189726.20887144317 2.6542\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4212 2 600515.7272112778 5.0428\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4212 2 530109.6772459471 3.8485\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4212 2 233631.6632422906 3.2513\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4212 2 460979.6893259023 3.5499\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4213 2 215168.35822559317 1.2229\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4213 2 31892.942846399004 0.6115\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4213 2 226374.35421382365 1.3406\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4213 2 166767.9542834762 0.976\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4214 3.0 1 37.8 1.246 206835.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4215 3.0 1 37.0 1.1853 234750.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4215 4.0 1 37.0 1.1652 234750.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4215 5.0 1 37.0 1.1455 234750.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4215 6.0 1 37.0 1.126 234750.1\n",
      "I am working on tract number 4220 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4228\n",
      "we have 2 non-opposing shorted wedges for tract no 4230\n",
      "we have 2 non-opposing shorted wedges for tract no 4231\n",
      "I am working on tract number 4240 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4243\n",
      "we have 2 non-opposing shorted wedges for tract no 4244\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4247 7.0 0 97.2 0.685 196040.9\n",
      "I am working on tract number 4260 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4261 2 204363.80257601303 2.0067\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4261 2 44303.13140734294 1.0034\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4261 1 383832.29155552963 2.9095\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4261 2 287955.9875629194 2.043\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4261 1 48276.85139611026 1.4548\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4261 2 48124.85490044355 1.5232\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4261 1 426605.89474795404 3.4948\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4261 2 73410.87775496826 1.7831\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4261 1 373506.64026667457 2.4748\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4261 2 109611.00333616833 1.9131\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4261 1 78964.27387337509 1.9648\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4261 2 162294.2707905504 1.978\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4261 1 129314.01321148273 2.2198\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4261 2 211514.39700138796 2.0105\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4261 1 357879.5329458186 2.3473\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4261 2 181665.90960959997 1.9943\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4261 1 272352.4818057566 2.2835\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4261 2 196338.2689221515 2.0024\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4261 1 199843.2733154051 2.2516\n",
      "14 yoyos for tract,wedge,wedgePop,r= 4261 2 188942.86835462152 1.9983\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4261 1 158872.43849421683 2.2357\n",
      "15 yoyos for tract,wedge,wedgePop,r= 4261 2 192628.9906127183 2.0004\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4261 1 178757.8900467967 2.2437\n",
      "16 yoyos for tract,wedge,wedgePop,r= 4261 2 190783.51235876358 1.9994\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4270 3 39250.315500291064 1.1249\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4270 3 781400.2209322759 3.9708\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4270 3 419480.8809308806 2.5478\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4270 3 352649.1943974799 1.8364\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4270 3 56978.744765513344 1.4806\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4270 3 81363.50525635459 1.6585\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4270 3 192785.3104181309 1.7474\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4270 3 274273.7822434144 1.7919\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4270 3 232821.59607815696 1.7697\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4271 3 460243.05244433024 2.6464\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4271 3 43804.68459199043 1.3232\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4271 3 431078.6354166259 2.488\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4271 3 376499.9227076027 1.9056\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4271 3 222755.11515730017 1.6144\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4271 3 58147.81553212178 1.4688\n",
      "loop31.0, tr4271,wedgePops650513.69, 137630.6, 209259.7, 209550.0, 94073.4, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?01310 \n",
      "   targetWP, latest drx4 are tWP,dr, 209775.4,0.4801, 209775.4,0.779, 209775.4,-0.0097, 209775.4,0.0728\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4271 3 94073.43779130204 1.5416\n",
      "loop32.0, tr4271,wedgePops713877.07, 137630.6, 209259.7, 209550.0, 157436.8, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?01310 \n",
      "   targetWP, latest drx4 are tWP,dr, 209775.4,0.4801, 209775.4,0.779, 209775.4,-0.0097, 209775.4,0.0364\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4271 3 157436.80968639965 1.578\n",
      "loop33.0, tr4271,wedgePops744893.25, 137630.6, 209259.7, 209550.0, 188453.0, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?01310 \n",
      "   targetWP, latest drx4 are tWP,dr, 209775.4,0.4801, 209775.4,0.779, 209775.4,-0.0097, 209775.4,0.0182\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4271 3 188452.99116657447 1.5962\n",
      "loop34.0, tr4271,wedgePops760983.94, 137630.6, 209259.7, 209550.0, 204543.7, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?01310 \n",
      "   targetWP, latest drx4 are tWP,dr, 209775.4,0.4801, 209775.4,0.779, 209775.4,-0.0097, 209775.4,0.0091\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4271 3 204543.68724809264 1.6053\n",
      "loop35.0, tr4271,wedgePops770093.95, 137630.6, 209259.7, 209550.0, 213653.7, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?01310 \n",
      "   targetWP, latest drx4 are tWP,dr, 209775.4,0.4801, 209775.4,0.779, 209775.4,-0.0097, 209775.4,0.0046\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4273 1 386304.5405538643 2.2584\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4273 1 22588.361251610157 1.1292\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4273 1 396184.38851632626 2.4619\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4273 1 48138.56522865116 1.7956\n",
      "I am working on tract number 4280 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4281 2 479661.0189307226 2.9524\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4281 2 35056.085802595015 1.4762\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4281 2 489257.49437305867 3.0492\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4281 2 393181.30502015003 2.2627\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4281 2 90221.9532685926 1.8695\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4281 2 154388.47145127744 2.0661\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4281 2 287971.36178422644 2.1644\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4281 2 212254.476583671 2.1152\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4281 2 184725.49584369772 2.0907\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4281 2 198284.18262912557 2.103\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4285 8.0 2 90.0 5.6608 205324.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4286 12.0 3 90.0 6.2245 215354.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4286 13.0 3 90.0 5.6977 215354.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4286 22.0 0 88.5 5.594 201113.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4293 1 224384.9117480656 4.048\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4293 1 28765.701106745546 2.024\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4293 1 223601.812714712 4.0132\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4293 1 119548.96371068966 3.0186\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4293 1 154325.04540223553 3.5159\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4293 1 171060.8286397563 3.7646\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4293 1 219023.6568167554 3.8889\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4293 1 200286.86430200984 3.8267\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4293 1 184169.3117489869 3.7956\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4295 1 201005.51475341045 1.5416\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4295 1 111706.11951282524 0.7708\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4295 1 224143.38271844914 1.6077\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4295 1 123533.64540716517 1.1893\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4295 1 136077.29839092027 1.3985\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4295 1 166152.97466225602 1.5031\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4295 1 212978.885784144 1.5554\n",
      "I am working on tract number 4300 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4316 1 246876.5505305932 1.4714\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4316 1 134872.68917518778 0.7357\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4316 1 247433.3036092943 1.4787\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4316 1 161320.34061075898 1.1072\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4316 1 175698.08075884156 1.293\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4316 1 240025.06160161167 1.3859\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4316 1 212276.9958403954 1.3394\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4317 9.0 0 90.0 5.8682 221612.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4317 19.0 0 86.5 6.0807 216887.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4317 20.0 0 86.5 5.661 216887.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4318 25.0 0 84.3 7.3642 242098.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4318 26.0 0 84.3 6.1484 242098.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4318 0 242098.74535320146 9.8957\n",
      "loop31.0, tr4318,wedgePops983446.92, 158856.1, 441238.7, 191866.1, 191486.0, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0100 \n",
      "   targetWP, latest drx4 are tWP,dr, 202700.3,-4.9478, 202700.3,-3.1411, 202700.3,-0.0113, 202700.3,0.0287\n",
      "loop32.0, tr4318,wedgePops827533.27, 158856.1, 183297.2, 285326.9, 200053.1, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0110 \n",
      "   targetWP, latest drx4 are tWP,dr, 202700.3,-4.9478, 202700.3,-0.9901, 202700.3,0.1705, 202700.3,0.2047\n",
      "loop33.0, tr4318,wedgePops743820.5, 158856.1, 183378.2, 199824.7, 201761.5, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0220 \n",
      "   targetWP, latest drx4 are tWP,dr, 202700.3,-4.9478, 202700.3,0.0652, 202700.3,-0.1201, 202700.3,0.0477\n",
      "loop34.0, tr4318,wedgePops1013072.18, 158856.1, 450444.5, 201230.0, 202541.6, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0230 \n",
      "   targetWP, latest drx4 are tWP,dr, 202700.3,-4.9478, 202700.3,1.2776, 202700.3,0.0033, 202700.3,0.0206\n",
      "loop35.0, tr4318,wedgePops747638.87, 158856.1, 183757.0, 202484.2, 202541.6, Overedge?1, 0, 0, 0, ,Satisfied?0001,yoyo?0330 \n",
      "   targetWP, latest drx4 are tWP,dr, 202700.3,-4.9478, 202700.3,-0.9393, 202700.3,0.0028, 202700.3,0.0206\n",
      "loop36.0, tr4318,wedgePops747706.06, 158856.1, 183824.2, 202484.2, 202541.6, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0430 \n",
      "   targetWP, latest drx4 are tWP,dr, 202700.3,-4.9478, 202700.3,0.0578, 202700.3,0.0028, 202700.3,0.0206\n",
      "loop37.0, tr4318,wedgePops1867559.58, 158856.1, 1303677.7, 202484.2, 202541.6, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0430 \n",
      "   targetWP, latest drx4 are tWP,dr, 202700.3,-4.9478, 202700.3,7.0314, 202700.3,0.0028, 202700.3,0.0206\n",
      "loop38.0, tr4318,wedgePops1032086.93, 158856.1, 468205.0, 202484.2, 202541.6, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0530 \n",
      "   targetWP, latest drx4 are tWP,dr, 202700.3,-4.9478, 202700.3,-5.4696, 202700.3,0.0028, 202700.3,0.0206\n",
      "loop39.0, tr4318,wedgePops741825.98, 158856.1, 177944.1, 202484.2, 202541.6, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0530 \n",
      "   targetWP, latest drx4 are tWP,dr, 202700.3,-4.9478, 202700.3,-2.5424, 202700.3,0.0028, 202700.3,0.0206\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 4.94785 242098.7454 158856.0854 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 4.26662 468205.0213 177944.0785 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 2.54117 201229.9841 202484.2116 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 2.32043 201761.5096 202541.6077 0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop40.0, tr4318,wedgePops744561.75, 158856.1, 180679.8, 202484.2, 202541.6, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0630 \n",
      "   targetWP, latest drx4 are tWP,dr, 202700.3,-4.9478, 202700.3,0.2182, 202700.3,0.0028, 202700.3,0.0206\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 4.94785 242098.7454 158856.0854 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 4.4848 177944.0785 180679.8434 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 2.54117 201229.9841 202484.2116 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 2.32043 201761.5096 202541.6077 0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 4318, giving up w pop 744561.7481982694\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4319 14.0 1 -272.4 5.9032 219933.4\n",
      "I am working on tract number 4320 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4320 12.0 3 90.0 6.2083 205224.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4321 8.0 1 88.3 1.9705 264591.9\n",
      "I am working on tract number 4340 of 6896 tracts\n",
      "I am working on tract number 4360 of 6896 tracts\n",
      "I am working on tract number 4380 of 6896 tracts\n",
      "I am working on tract number 4400 of 6896 tracts\n",
      "I am working on tract number 4420 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4438\n",
      "I am working on tract number 4440 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4459 22.0 1 -272.4 5.6991 257821.5\n",
      "I am working on tract number 4460 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4460 16.0 2 90.0 7.2301 248299.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4460 17.0 2 90.0 5.9152 248299.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4460 27.0 0 84.7 5.5046 234567.2\n",
      "loop31.0, tr4460,wedgePops770212.35, 136765.6, 207820.0, 215437.3, 210189.4, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0123 \n",
      "   targetWP, latest drx4 are tWP,dr, 210063.8,-0.7917, 210063.8,-1.0111, 210063.8,-0.0605, 210063.8,-0.0009\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4464 3 52153.25502767059 1.2213\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4464 3 916988.6980804601 4.391\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4464 3 440569.5053260197 2.8061\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4464 3 87609.51536699565 2.0137\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4464 3 408204.12171221484 2.4099\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4464 3 176038.00260955576 2.2118\n",
      "loop31.0, tr4464,wedgePops916410.03, 166255.5, 200030.4, 200122.3, 350001.7, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?02011 \n",
      "   targetWP, latest drx4 are tWP,dr, 200233.8,0.239, 200233.8,0.0211, 200233.8,0.0092, 200233.8,0.0991\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4464 3 350001.742179704 2.3109\n",
      "loop32.0, tr4464,wedgePops829092.28, 166255.5, 200030.4, 200122.3, 262684.0, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?02012 \n",
      "   targetWP, latest drx4 are tWP,dr, 200233.8,0.239, 200233.8,0.0211, 200233.8,0.0092, 200233.8,-0.0495\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4464 3 262683.99818217434 2.2614\n",
      "loop33.0, tr4464,wedgePops786896.4, 166255.5, 200030.4, 200122.3, 220488.1, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?02012 \n",
      "   targetWP, latest drx4 are tWP,dr, 200233.8,0.239, 200233.8,0.0211, 200233.8,0.0092, 200233.8,-0.0248\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4464 3 220488.12166007207 2.2366\n",
      "loop34.0, tr4464,wedgePops763903.11, 166255.5, 200030.4, 200122.3, 197494.8, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?02012 \n",
      "   targetWP, latest drx4 are tWP,dr, 200233.8,0.239, 200233.8,0.0211, 200233.8,0.0092, 200233.8,-0.0124\n",
      "I am working on tract number 4480 of 6896 tracts\n",
      "I am working on tract number 4500 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4514 2 212859.06861807354 1.4199\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4517 2.0 0 90.0 0.5505 230735.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4517 3.0 0 90.0 0.4775 230735.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4517 1 349413.4941215222 1.7339\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4517 1 38655.16943557694 0.8669\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4517 1 428483.8203997675 2.6245\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4517 1 351812.2290950927 1.7457\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4517 1 40187.19417160109 1.3063\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4517 1 124126.57081274306 1.526\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4517 1 304292.5314485072 1.6359\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4517 1 249783.1321158895 1.5809\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4517 1 184895.29541697964 1.5535\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4517 1 219920.76101482744 1.5672\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4517 1 202418.76772968413 1.5603\n",
      "I am working on tract number 4520 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4521\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4522 3 521650.7383236052 2.156\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4522 3 19830.617772220285 1.078\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4522 3 519638.93184715876 2.1387\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4522 3 84048.03643868153 1.6083\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4522 3 121462.52240834845 1.8735\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4522 3 466265.51299967547 2.0061\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4522 3 251940.82388312573 1.9398\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4522 3 162289.54606243374 1.9066\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4522 3 206427.35888425366 1.9232\n",
      "14 yoyos for tract,wedge,wedgePop,r= 4522 3 183075.1794628501 1.9149\n",
      "we have 2 non-opposing shorted wedges for tract no 4534\n",
      "I am working on tract number 4540 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4542 3 389770.23821201466 3.4615\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4542 3 2984.1000405612285 1.7308\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4542 3 423963.3149037912 4.6698\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4542 3 353484.4191982241 3.2003\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4542 3 10065.342777237529 2.4655\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4542 3 73100.50540774371 2.8329\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4542 3 332437.4183020509 3.0166\n",
      "loop31.0, tr4542,wedgePops802246.61, 123238.0, 214405.9, 215026.7, 249575.9, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?01111 \n",
      "   targetWP, latest drx4 are tWP,dr, 214572.9,1.2776, 214572.9,0.0003, 214572.9,-0.0586, 214572.9,-0.0918\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4542 3 249575.91429610446 2.9247\n",
      "loop32.0, tr4542,wedgePops702547.83, 123238.0, 214405.9, 215026.7, 149877.1, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?01111 \n",
      "   targetWP, latest drx4 are tWP,dr, 214572.9,1.2776, 214572.9,0.0003, 214572.9,-0.0586, 214572.9,-0.0459\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4542 3 149877.135382828 2.8788\n",
      "loop33.0, tr4542,wedgePops756547.67, 123238.0, 214405.9, 215026.7, 203877.0, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?01112 \n",
      "   targetWP, latest drx4 are tWP,dr, 214572.9,1.2776, 214572.9,0.0003, 214572.9,-0.0586, 214572.9,0.023\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4542 3 203876.975897072 2.9018\n",
      "loop34.0, tr4542,wedgePops779373.71, 123238.0, 214405.9, 215026.7, 226703.0, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?01112 \n",
      "   targetWP, latest drx4 are tWP,dr, 214572.9,1.2776, 214572.9,0.0003, 214572.9,-0.0586, 214572.9,0.0115\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4542 3 226703.0224944809 2.9133\n",
      "loop35.0, tr4542,wedgePops767983.39, 123238.0, 214405.9, 215026.7, 215312.7, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?01113 \n",
      "   targetWP, latest drx4 are tWP,dr, 214572.9,1.2776, 214572.9,0.0003, 214572.9,-0.0586, 214572.9,-0.0057\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4544 9.0 0 90.0 8.5552 218675.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4544 3 9350.255221910891 2.3083\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4544 3 1141999.0160696842 9.905\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4544 3 1131425.861576738 6.1067\n",
      "loop31.0, tr4544,wedgePops1055204.53, 159926.6, 202007.9, 202121.3, 491148.8, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0218 \n",
      "   targetWP, latest drx4 are tWP,dr, 202343.4,1.2776, 202343.4,0.0019, 202343.4,0.0018, 202343.4,-1.8992\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4544 3 491148.7884383295 4.2075\n",
      "loop32.0, tr4544,wedgePops789242.19, 159926.6, 202007.9, 202121.3, 225186.4, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0218 \n",
      "   targetWP, latest drx4 are tWP,dr, 202343.4,1.2776, 202343.4,0.0019, 202343.4,0.0018, 202343.4,-0.9496\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4544 3 225186.4405596552 3.2579\n",
      "loop33.0, tr4544,wedgePops587794.74, 159926.6, 202007.9, 202121.3, 23739.0, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0218 \n",
      "   targetWP, latest drx4 are tWP,dr, 202343.4,1.2776, 202343.4,0.0019, 202343.4,0.0018, 202343.4,-0.4748\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4544 3 23738.992106482212 2.7831\n",
      "loop34.0, tr4544,wedgePops625127.56, 159926.6, 202007.9, 202121.3, 61071.8, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0219 \n",
      "   targetWP, latest drx4 are tWP,dr, 202343.4,1.2776, 202343.4,0.0019, 202343.4,0.0018, 202343.4,0.2374\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4544 3 61071.812707769335 3.0205\n",
      "loop35.0, tr4544,wedgePops643152.31, 159926.6, 202007.9, 202121.3, 79096.6, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0219 \n",
      "   targetWP, latest drx4 are tWP,dr, 202343.4,1.2776, 202343.4,0.0019, 202343.4,0.0018, 202343.4,0.1187\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4544 3 79096.56015171956 3.1392\n",
      "loop36.0, tr4544,wedgePops679922.97, 159926.6, 202007.9, 202121.3, 115867.2, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0219 \n",
      "   targetWP, latest drx4 are tWP,dr, 202343.4,1.2776, 202343.4,0.0019, 202343.4,0.0018, 202343.4,0.0593\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4544 3 115867.22360281461 3.1985\n",
      "loop37.0, tr4544,wedgePops724929.93, 159926.6, 202007.9, 202121.3, 160874.2, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0219 \n",
      "   targetWP, latest drx4 are tWP,dr, 202343.4,1.2776, 202343.4,0.0019, 202343.4,0.0018, 202343.4,0.0297\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4544 3 160874.18304966032 3.2282\n",
      "loop38.0, tr4544,wedgePops752080.38, 159926.6, 202007.9, 202121.3, 188024.6, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0219 \n",
      "   targetWP, latest drx4 are tWP,dr, 202343.4,1.2776, 202343.4,0.0019, 202343.4,0.0018, 202343.4,0.0148\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4544 3 188024.63350956043 3.243\n",
      "loop39.0, tr4544,wedgePops769340.46, 159926.6, 202007.9, 202121.3, 205284.7, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0219 \n",
      "   targetWP, latest drx4 are tWP,dr, 202343.4,1.2776, 202343.4,0.0019, 202343.4,0.0018, 202343.4,0.0074\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 7.63728 104521.9928 159926.5773 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 2.53884 198550.6765 202007.8532 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 3.25672 200828.3323 202121.3145 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 3.25046 188024.6335 205284.7189 0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 yoyos for tract,wedge,wedgePop,r= 4546 0 57398.233038748614 2.7109\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4546 0 1633775.1362033924 13.2972\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4546 21.0 0 122.7 8.0041 1633775.1\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4546 0 1633775.1362033924 8.0041\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4546 0 1109336.765160067 5.3575\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4546 0 329958.79101410264 4.0342\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4546 0 311313.59848807927 3.3725\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4546 0 94616.81412605161 3.0417\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4546 0 215392.69862455732 3.2071\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4546 0 145342.04393029487 3.1244\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4546 0 169185.7167935823 3.1658\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4546 0 191073.4500506449 3.1865\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4546 0 202680.36978606926 3.1968\n",
      "loop31.0, tr4546,wedgePops768975.22, 208854.2, 205560.6, 205577.5, 148982.9, Overedge?0, 0, 0, 1, ,Satisfied?0110,yoyo?11610 \n",
      "   targetWP, latest drx4 are tWP,dr, 205991.3,0.0052, 205991.3,0.0007, 205991.3,0.0044, 205991.3,1.2776\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4548 1 248107.9136495688 2.456\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4548 1 2287029.11574786 5.995\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4548 1 1158992.4591565703 4.2255\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4548 1 375614.36548531323 3.3408\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4548 1 336167.5220350177 2.8984\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4548 1 331414.74796265276 2.6772\n",
      "loop31.0, tr4548,wedgePops816961.01, 19686.8, 328888.4, 278553.6, 189832.2, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0800 \n",
      "   targetWP, latest drx4 are tWP,dr, 278718.9,0.3062, 278718.9,-0.1106, 278718.9,0.0012, 278718.9,1.2776\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4548 1 328888.4010260739 2.5666\n",
      "loop32.0, tr4548,wedgePops804308.66, 19686.8, 316236.1, 278553.6, 189832.2, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0800 \n",
      "   targetWP, latest drx4 are tWP,dr, 278718.9,0.3062, 278718.9,-0.0553, 278718.9,0.0012, 278718.9,1.2776\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4548 1 316236.0588472097 2.5113\n",
      "loop33.0, tr4548,wedgePops786158.88, 19686.8, 298086.3, 278553.6, 189832.2, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0800 \n",
      "   targetWP, latest drx4 are tWP,dr, 278718.9,0.3062, 278718.9,-0.0276, 278718.9,0.0012, 278718.9,1.2776\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4548 1 298086.27956988226 2.4837\n",
      "loop34.0, tr4548,wedgePops764595.82, 19686.8, 276523.2, 278553.6, 189832.2, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0800 \n",
      "   targetWP, latest drx4 are tWP,dr, 278718.9,0.3062, 278718.9,-0.0138, 278718.9,0.0012, 278718.9,1.2776\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4558 3 688855.5989134798 2.2847\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4558 3 80027.12179587991 1.1423\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4558 3 683423.3256271579 2.2332\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4558 3 612115.85134988 1.6878\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4558 3 401569.3897736877 1.4151\n",
      "I am working on tract number 4560 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4560 1 433894.7782005168 13.6812\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4560 1 321927.2403347864 6.8406\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4560 1 124328.29794403154 3.4203\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4560 1 260610.63003565284 5.5215\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4560 1 126683.22728904962 4.4709\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4560 1 131617.02141626255 4.9962\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4560 1 224218.94712170435 5.2588\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4560 1 138488.96162867523 5.1275\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4560 1 184206.27807910004 5.1932\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4560 1 212976.55996697492 5.226\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4560 1 200899.886504116 5.2096\n",
      "we have 2 non-opposing shorted wedges for tract no 4571\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4571 3 620250.4352876802 2.388\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4571 3 133046.2048850282 1.194\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4571 3 821495.9518879143 3.2215\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4571 3 231374.11176329583 2.2078\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4571 3 718829.54335502 2.7146\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4571 3 634439.3833802321 2.4612\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4571 3 519724.7659471582 2.3345\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4571 3 332028.00995228556 2.2711\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4571 3 260245.83868308237 2.2394\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4571 3 294922.10394492885 2.2553\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4571 3 313453.5637749966 2.2632\n",
      "we have 2 non-opposing shorted wedges for tract no 4579\n",
      "I am working on tract number 4580 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4584\n",
      "we have 2 non-opposing shorted wedges for tract no 4585\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4596 5.0 3 90.0 3.7177 214959.1\n",
      "I am working on tract number 4600 of 6896 tracts\n",
      "I am working on tract number 4620 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4620 9.0 1 49.1 1.9676 211644.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4631 10.0 1 94.1 2.3089 383149.4\n",
      "I am working on tract number 4640 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4642 3 428594.7718349042 2.7857\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4642 3 143151.95605685748 1.3928\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4642 3 424247.22230495757 2.7299\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4642 3 238399.37358755543 2.0614\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4642 3 418410.30923823453 2.3956\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4642 3 411244.1328211535 2.2285\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4642 3 348611.62384273065 2.1449\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4642 3 278631.0117810634 2.1032\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4642 3 253866.67815332854 2.0823\n",
      "I am working on tract number 4660 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4672 0 278576.786841541 2.7634\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4672 0 102761.77106741618 1.3817\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4672 0 272021.3844366493 2.6517\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4672 0 135092.21768025606 2.0167\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4672 0 254279.79567247408 2.3342\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4672 0 215980.7210350886 2.1754\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4672 0 155820.34007437588 2.096\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4672 0 186067.50651296542 2.1357\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4672 0 201742.29812376178 2.1556\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4678 2 286708.08495426376 3.0113\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4678 2 124578.71343855764 1.5056\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4678 2 286688.5635158628 3.0108\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4678 2 262461.42510880134 2.2582\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4678 2 136560.26374748 1.8819\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4678 2 225615.77465333417 2.0701\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4678 2 172883.73839144435 1.976\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4678 2 212213.7173259945 2.023\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4678 2 197170.58047495718 1.9995\n",
      "14 yoyos for tract,wedge,wedgePop,r= 4678 2 185481.9304815037 1.9877\n",
      "I am working on tract number 4680 of 6896 tracts\n",
      "I am working on tract number 4700 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4705 3 201298.74428380022 2.5379\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4705 3 171398.30961653317 1.269\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4705 3 201284.2630048193 2.5307\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4705 3 178277.28380739255 1.8999\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4717 1 272499.68000443955 4.0215\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4717 1 54139.48379797573 2.0108\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4717 1 272378.3772245717 4.0128\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4717 1 83802.50541519694 3.0118\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4717 1 255152.80387580546 3.5123\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4717 1 220893.11998863053 3.262\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4717 1 134909.6873796975 3.1369\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4717 1 199759.09256276197 3.1995\n",
      "14 yoyos for tract,wedge,wedgePop,r= 4717 1 173109.02042302417 3.1682\n",
      "I am working on tract number 4720 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4730\n",
      "I am working on tract number 4740 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4747\n",
      "I am working on tract number 4760 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4772\n",
      "I am working on tract number 4780 of 6896 tracts\n",
      "I am working on tract number 4800 of 6896 tracts\n",
      "I am working on tract number 4820 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4839 3 559237.3288775807 2.1123\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4839 3 18874.237851452082 1.0562\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4839 3 571961.6490291692 2.1383\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4839 3 395932.5464375708 1.5972\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4839 3 67177.93186952407 1.3267\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4839 3 103431.3368646176 1.462\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4839 3 159369.0289137105 1.5296\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4839 3 268607.31195755827 1.5634\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4839 3 208328.6149288584 1.5465\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4839 3 179655.58521616802 1.5381\n",
      "I am working on tract number 4840 of 6896 tracts\n",
      "I am working on tract number 4860 of 6896 tracts\n",
      "I am working on tract number 4880 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4881 3 295661.9110254865 2.2168\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4881 3 99053.89198273892 1.1084\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4881 3 296193.41892191826 2.2285\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4881 3 242794.5558183217 1.6684\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4881 3 112229.24008990922 1.3884\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4881 3 159195.6343443496 1.5284\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4881 3 215127.7633814972 1.5984\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4883 3 270751.71924463904 2.3652\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4883 3 96350.28979166914 1.1826\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4883 3 271793.4856229158 2.3953\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4883 3 129673.83961913834 1.789\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4883 3 235271.41453685428 2.0921\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4883 3 157156.9144224213 1.9406\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4883 3 215222.4617226104 2.0163\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4885 2 273214.50506309525 2.7352\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4885 2 112691.66683407367 1.3676\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4885 2 272061.5316747243 2.5934\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4885 2 203718.921561179 1.9805\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4885 2 124725.089319137 1.674\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4885 2 129019.57949456385 1.8273\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4885 2 138679.7831259815 1.9039\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4885 2 168150.2900294529 1.9422\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4885 2 187873.1050307667 1.9614\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4885 2 197133.04695723997 1.9709\n",
      "I am working on tract number 4900 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 3.0 0 89.0 1.1256 196762.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 4.0 0 89.0 1.1039 196762.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 5.0 0 89.0 1.0826 196762.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 6.0 0 89.0 1.0618 196762.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 7.0 0 89.0 1.0413 196762.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 8.0 0 89.0 1.0212 196762.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 9.0 0 89.0 1.0015 196762.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 10.0 0 89.0 0.9822 196762.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 11.0 0 89.0 0.9633 196762.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 12.0 0 89.0 0.9448 196762.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 13.0 0 89.0 0.9265 196762.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 14.0 0 89.0 0.9087 196762.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 15.0 0 89.0 0.8912 196762.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 16.0 0 89.0 0.874 196762.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 17.0 0 89.0 0.8572 196762.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 18.0 0 89.0 0.8406 196762.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 19.0 0 89.0 0.8244 196762.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 20.0 0 89.0 0.8086 196762.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 21.0 0 89.0 0.793 196762.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4912 22.0 0 89.0 0.7777 196762.6\n",
      "I am working on tract number 4920 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4923 3 277862.44096552546 2.509\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4923 3 20260.307763133635 1.2545\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4923 3 512144.83861542935 2.6039\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4923 3 40183.9418958241 1.9292\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4923 3 102213.9775541025 2.2665\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4923 3 151497.39550201973 2.4352\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4923 3 324347.0834103954 2.5195\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4923 3 182449.44984671302 2.4774\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4923 3 236180.2653782952 2.4984\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4923 3 277942.7628049103 2.509\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4923 3 256129.15046655425 2.5037\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4934 3 261266.00037483213 1.5842\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4934 3 31292.561053784215 0.7921\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4934 3 372938.6580454601 1.623\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4934 3 72854.71758237225 1.2075\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4934 3 123786.72033777804 1.4153\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4934 3 178072.46479694947 1.5191\n",
      "I am working on tract number 4940 of 6896 tracts\n",
      "I am working on tract number 4960 of 6896 tracts\n",
      "I am working on tract number 4980 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4998\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4998 1 754137.1857900007 2.9539\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4998 1 133738.12261709513 1.477\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4998 1 799338.5148865817 3.2625\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4998 1 617102.1277557566 2.3697\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4998 1 181067.64694948326 1.9234\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4998 1 216884.3606338183 2.1466\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4998 1 391652.71078981797 2.2582\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4998 1 259237.2295271511 2.2024\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4998 1 322066.2918807475 2.2303\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4998 1 353995.1278530416 2.2442\n",
      "14 yoyos for tract,wedge,wedgePop,r= 4998 1 337325.88187469204 2.2372\n",
      "I am working on tract number 5000 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5004 1 1365078.4784928283 4.4624\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5004 1 513760.8197329808 2.6959\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5004 1 407669.63693298155 1.8127\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5004 1 51663.772394641535 1.3711\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5004 1 59069.536479589704 1.5919\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5004 1 326693.4539622193 1.7023\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5004 1 124793.78338398921 1.6471\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5004 1 218300.97788597236 1.6747\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5004 1 275138.8222852046 1.6885\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5004 1 300639.8687900937 1.6954\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5004 1 313914.31513393915 1.6988\n",
      "we have 2 non-opposing shorted wedges for tract no 5017\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5019 1 647616.8150908601 2.6836\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5019 1 40495.661530375015 1.3418\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5019 1 645579.1494708201 2.6666\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5019 1 415173.68808613054 2.0042\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5019 1 80048.638298798 1.673\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5019 1 94502.65784840562 1.8386\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5019 1 144814.41456561597 1.9214\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5019 1 268363.32705506124 1.9628\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5019 1 198658.66140669156 1.9421\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5019 1 232439.2823979204 1.9525\n",
      "13 yoyos for tract,wedge,wedgePop,r= 5019 1 250342.27217939874 1.9576\n",
      "14 yoyos for tract,wedge,wedgePop,r= 5019 1 241391.52374733158 1.955\n",
      "I am working on tract number 5020 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5027 3 1375893.2909329513 3.5887\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5027 3 239523.2225308956 1.7943\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5027 3 6959.060472770245 0.8972\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5027 3 91256.58820677151 1.6589\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5027 3 279879.7688125252 2.0398\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5027 3 268127.5132954254 1.8494\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5027 3 210035.28884225077 1.7541\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5027 3 131656.16816837282 1.7065\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5027 3 176684.78413086437 1.7303\n",
      "I am working on tract number 5040 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5040 7.0 1 80.7 2.0569 328168.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5043 0 505084.3732690878 3.3163\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5043 0 31497.308221041632 1.6582\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5043 0 504600.13558245887 3.3106\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5043 0 90504.96530454553 2.4844\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5043 0 457840.73730720533 2.8975\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5043 0 308839.09735257097 2.6909\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5043 0 139715.3735847341 2.5877\n",
      "13 yoyos for tract,wedge,wedgePop,r= 5043 0 208799.60985522383 2.6393\n",
      "14 yoyos for tract,wedge,wedgePop,r= 5043 0 169780.53368297103 2.6135\n",
      "14 yoyos for tract,wedge,wedgePop,r= 5043 0 186823.1322985109 2.6264\n",
      "15 yoyos for tract,wedge,wedgePop,r= 5043 0 196862.86031731206 2.6328\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5045 0 302328.6637407518 2.8224\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5045 0 21222.70598223299 1.4112\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5045 0 351637.0493544341 2.9363\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5045 0 56385.05998622195 2.1737\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5045 0 89693.64023766964 2.555\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5045 0 206840.33659628005 2.7457\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5045 0 113379.08953026132 2.6503\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5045 0 166001.491145748 2.698\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5045 0 184651.34722980188 2.7218\n",
      "I am working on tract number 5060 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5063 0 464682.05333126767 1.0001\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5063 0 42458.21146703552 0.5001\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5063 0 460650.87689209083 0.9656\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5063 0 432637.74198322475 0.7328\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5063 0 184396.60623540383 0.6165\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5063 0 341390.7910019058 0.6747\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5063 0 266825.38614427473 0.6456\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5063 0 223989.98966512742 0.631\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5063 0 203019.72683150598 0.6237\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5069 3 245698.2873546262 0.5343\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5069 3 10829.147224696673 0.2671\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5069 3 417237.8685178041 0.7044\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5069 3 122074.60156883794 0.4858\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5069 3 355655.684666705 0.5951\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5069 3 257467.7363169848 0.5405\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5069 3 196621.2178593623 0.5131\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5069 3 158541.696022021 0.4995\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5069 3 177215.83075080058 0.5063\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5069 3 186860.28730923356 0.5097\n",
      "I am working on tract number 5080 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 5084\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5089 1 504142.9997572355 1.8119\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5089 1 30164.282513039245 0.9059\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5089 1 691703.8022883995 2.5847\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5089 1 427976.66997227515 1.7453\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5089 1 42122.64075055014 1.3256\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5089 1 94549.0623276467 1.5355\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5089 1 246082.27498600207 1.6404\n",
      "we have 2 non-opposing shorted wedges for tract no 5093\n",
      "we have 2 non-opposing shorted wedges for tract no 5095\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5095 3 871922.0291883044 3.5957\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5095 3 60074.8089329364 1.7979\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5095 3 738563.0614487746 3.2463\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5095 3 641625.4418691065 2.5221\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5095 3 443090.40712393774 2.16\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5095 3 175676.9209068407 1.9789\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5095 3 337656.6092479512 2.0695\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5095 3 393499.3519482183 2.1147\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5095 3 364647.5505420602 2.0921\n",
      "I am working on tract number 5100 of 6896 tracts\n",
      "I am working on tract number 5120 of 6896 tracts\n",
      "I am working on tract number 5140 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5145 6.0 3 66.1 1.9745 205185.3\n",
      "I am working on tract number 5160 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5161 6.0 3 90.0 1.7228 201011.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5161 11.0 3 90.0 1.7497 201011.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5161 16.0 3 90.0 1.7433 201011.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5161 3 201011.41370234825 1.7832\n",
      "I am working on tract number 5180 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5180 11.0 2 90.0 3.081 331174.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5180 2 331174.4419787649 4.4921\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5180 16.0 2 90.0 2.246 331174.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5180 2 331174.4419787649 2.246\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5180 20.0 0 85.0 2.2369 318648.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5197 3 675588.4544516166 13.7578\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5197 3 572121.6029869925 6.8789\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5197 3 134634.51108045486 3.4395\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5197 3 443050.9728069779 5.4548\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5197 3 138920.76831081923 4.4471\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5197 3 146352.3785945261 4.9509\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5197 3 181362.72513398615 5.2029\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5197 3 307516.8347247515 5.3288\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5197 3 261785.4686768099 5.2658\n",
      "I am working on tract number 5200 of 6896 tracts\n",
      "I am working on tract number 5220 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5232 3 239344.52665093617 3.0155\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5232 3 68022.53816909672 1.5078\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5232 3 279801.70200363966 3.3812\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5232 3 100613.0588501541 2.4445\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5232 3 162920.57550422807 2.9128\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5232 3 251738.43295878934 3.147\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5232 3 240615.52184400326 3.0299\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5232 3 222080.50589101957 2.9713\n",
      "I am working on tract number 5240 of 6896 tracts\n",
      "I am working on tract number 5260 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5266 1 452415.9631086395 2.3328\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5266 1 41206.882612858666 1.1664\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5266 1 460182.9879452618 2.4043\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5266 1 276709.90889344644 1.7854\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5266 1 115210.05379814477 1.4759\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5266 1 212402.2997249805 1.6306\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5274 3 224579.8344168741 1.6925\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5274 3 94754.11691826077 0.8462\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5274 3 228806.6968917292 1.7217\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5274 3 118912.06315642985 1.284\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5274 3 131868.58018605955 1.5028\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5274 3 168167.10190727873 1.6122\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5274 3 213715.20898488557 1.667\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5276 2 227465.9160450624 3.2957\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5276 2 94882.11464992611 1.6478\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5276 2 227470.96042933746 3.2967\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5276 2 124041.10323856179 2.4723\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5276 2 184663.5511852432 2.8845\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5276 2 222994.32689315017 3.0906\n",
      "I am working on tract number 5280 of 6896 tracts\n",
      "I am working on tract number 5300 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5307 9.0 3 37.3 3.6962 353746.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5307 10.0 3 37.3 3.6559 353746.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5307 11.0 3 37.3 3.6161 353746.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5311 3 364871.54639977537 2.5428\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5311 3 149851.26092856028 1.2714\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5311 3 364862.9807524471 2.5112\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5311 3 361559.88163789164 1.8913\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5311 3 153414.01307244576 1.5813\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5311 3 224407.0240908375 1.7363\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5311 3 342617.66274476465 1.8138\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5311 3 273846.02042529534 1.7751\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5311 3 251710.89208852116 1.7557\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5311 3 240778.73486851962 1.746\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5312 3 315902.3020688415 1.9901\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5312 3 145375.90022063363 0.995\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5312 3 427194.89592631604 2.2621\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5312 3 161174.79721557413 1.6285\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5312 3 255997.1254200043 1.9453\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5312 3 162714.00024027136 1.7869\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5312 3 174111.59391804945 1.8661\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5312 3 198124.02147050164 1.9057\n",
      "13 yoyos for tract,wedge,wedgePop,r= 5312 3 230497.63580963263 1.9255\n",
      "14 yoyos for tract,wedge,wedgePop,r= 5312 3 212002.0206592136 1.9156\n",
      "14 yoyos for tract,wedge,wedgePop,r= 5312 3 220865.06229939946 1.9206\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5313 3 426754.76224831626 2.8826\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5313 3 157778.72108072636 1.4413\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5313 3 426611.1439051669 2.8659\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5313 3 423967.73661163915 2.1536\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5313 3 165324.16872399588 1.7974\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5313 3 390233.85379436496 1.9755\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5313 3 245079.46334224037 1.8865\n",
      "13 yoyos for tract,wedge,wedgePop,r= 5313 3 297501.0725215784 1.931\n",
      "13 yoyos for tract,wedge,wedgePop,r= 5313 3 266728.16431616584 1.9087\n",
      "I am working on tract number 5320 of 6896 tracts\n",
      "I am working on tract number 5340 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5343 1 278875.060843141 2.7892\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5343 1 101739.56180830576 1.3946\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5343 1 278168.69001746783 2.7841\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5343 1 146789.32868964094 2.0893\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5343 1 174270.11851940033 2.4367\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5343 1 220071.48766169228 2.6104\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5343 1 241346.61887188617 2.6972\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5343 1 272212.7165352801 2.7407\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5343 1 261800.3567600945 2.7189\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5345 1 422848.1322792887 2.963\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5345 1 150526.87392785528 1.4815\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5345 1 422680.53598107887 2.9592\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5345 1 152523.3047326586 2.2203\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5345 1 180861.33729763172 2.5898\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5345 1 210732.54280331847 2.7745\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5345 1 184973.99926536068 2.6821\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5345 1 185836.6283244877 2.7283\n",
      "we have 2 non-opposing shorted wedges for tract no 5353\n",
      "I am working on tract number 5360 of 6896 tracts\n",
      "I am working on tract number 5380 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 5393\n",
      "I am working on tract number 5400 of 6896 tracts\n",
      "I am working on tract number 5420 of 6896 tracts\n",
      "I am working on tract number 5440 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5447 0 1290487.1400195176 5.1386\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5447 14.0 0 90.0 2.8408 1290487.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5447 0 1290487.1400195176 2.8408\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5447 15.0 0 90.0 1.6918 1290487.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5447 0 1290487.1400195176 1.6918\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5447 1 411831.0147968073 3.7569\n",
      "loop31.0, tr5447,wedgePops678565.46, 236112.4, 147450.6, 236337.5, 58665.0, Overedge?0, 0, 0, 1, ,Satisfied?1010,yoyo?2740 \n",
      "   targetWP, latest drx4 are tWP,dr, 236097.3,-0.0015, 236097.3,-1.8785, 236097.3,-0.0004, 236097.3,0.9833\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5447 1 147450.58801865403 1.8785\n",
      "loop32.0, tr5447,wedgePops871437.1, 236112.4, 340322.2, 236337.5, 58665.0, Overedge?0, 0, 0, 1, ,Satisfied?1010,yoyo?2840 \n",
      "   targetWP, latest drx4 are tWP,dr, 236097.3,-0.0015, 236097.3,1.1128, 236097.3,-0.0004, 236097.3,0.9833\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5447 1 340322.2307869666 2.9913\n",
      "loop33.0, tr5447,wedgePops842324.44, 236112.4, 311209.6, 236337.5, 58665.0, Overedge?0, 0, 0, 1, ,Satisfied?1010,yoyo?2940 \n",
      "   targetWP, latest drx4 are tWP,dr, 236097.3,-0.0015, 236097.3,-0.5564, 236097.3,-0.0004, 236097.3,0.9833\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5447 1 311209.56907162577 2.4349\n",
      "loop34.0, tr5447,wedgePops835588.57, 236112.4, 304473.7, 236337.5, 58665.0, Overedge?0, 0, 0, 1, ,Satisfied?1010,yoyo?2940 \n",
      "   targetWP, latest drx4 are tWP,dr, 236097.3,-0.0015, 236097.3,-0.2782, 236097.3,-0.0004, 236097.3,0.9833\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5447 1 304473.6989648075 2.1567\n",
      "loop35.0, tr5447,wedgePops826739.66, 236112.4, 295624.8, 236337.5, 58665.0, Overedge?0, 0, 0, 1, ,Satisfied?1010,yoyo?2940 \n",
      "   targetWP, latest drx4 are tWP,dr, 236097.3,-0.0015, 236097.3,-0.1391, 236097.3,-0.0004, 236097.3,0.9833\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5447 1 295624.7845693031 2.0176\n",
      "loop36.0, tr5447,wedgePops803813.38, 236112.4, 272698.5, 236337.5, 58665.0, Overedge?0, 0, 0, 1, ,Satisfied?1010,yoyo?2940 \n",
      "   targetWP, latest drx4 are tWP,dr, 236097.3,-0.0015, 236097.3,-0.0696, 236097.3,-0.0004, 236097.3,0.9833\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5447 1 272698.50314655935 1.948\n",
      "loop37.0, tr5447,wedgePops742637.68, 236112.4, 211522.8, 236337.5, 58665.0, Overedge?0, 0, 0, 1, ,Satisfied?1010,yoyo?2940 \n",
      "   targetWP, latest drx4 are tWP,dr, 236097.3,-0.0015, 236097.3,-0.0348, 236097.3,-0.0004, 236097.3,0.9833\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5447 1 211522.80929094888 1.9132\n",
      "loop38.0, tr5447,wedgePops777933.59, 236112.4, 246818.7, 236337.5, 58665.0, Overedge?0, 0, 0, 1, ,Satisfied?1010,yoyo?21040 \n",
      "   targetWP, latest drx4 are tWP,dr, 236097.3,-0.0015, 236097.3,0.0174, 236097.3,-0.0004, 236097.3,0.9833\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5447 1 246818.71545943062 1.9306\n",
      "loop39.0, tr5447,wedgePops761419.69, 236112.4, 230304.8, 236337.5, 58665.0, Overedge?0, 0, 0, 1, ,Satisfied?1010,yoyo?21140 \n",
      "   targetWP, latest drx4 are tWP,dr, 236097.3,-0.0015, 236097.3,-0.0087, 236097.3,-0.0004, 236097.3,0.9833\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.99314 240830.9781 236112.3644 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 1.92192 246818.7155 230304.8209 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.24636 237406.1344 236337.4814 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 2.50654 58659.5428 58665.0281 1\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 yoyos for tract,wedge,wedgePop,r= 5447 1 230304.8209272997 1.9219\n",
      "loop40.0, tr5447,wedgePops769815.58, 236112.4, 238700.7, 236337.5, 58665.0, Overedge?0, 0, 0, 1, ,Satisfied?1010,yoyo?21240 \n",
      "   targetWP, latest drx4 are tWP,dr, 236097.3,-0.0015, 236097.3,0.0043, 236097.3,-0.0004, 236097.3,0.9833\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.99314 240830.9781 236112.3644 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 1.92627 230304.8209 238700.7071 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.24636 237406.1344 236337.4814 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 2.50654 58659.5428 58665.0281 1\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 5447, giving up w pop 769815.5810535742\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5448 1 346094.0311615146 1.8706\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5448 1 11797.776113117463 0.9353\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5448 1 346094.6063598544 1.8742\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5448 1 89137.95488174411 1.4048\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5448 1 345984.5211067036 1.6395\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5448 1 308725.28395685554 1.5221\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5448 1 154614.93245160356 1.4634\n",
      "13 yoyos for tract,wedge,wedgePop,r= 5448 1 241528.26831394265 1.4928\n",
      "I am working on tract number 5460 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5462 3 267157.4998107413 2.5712\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5462 3 32225.714685914747 1.2856\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5462 3 496181.43417122524 2.6497\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5462 3 39505.84534661879 1.9677\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5462 3 90768.04590996748 2.3087\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5462 3 138723.3778884458 2.4792\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5463 1 287599.0263916904 1.686\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5463 1 191395.29827590197 0.843\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5463 1 287978.6828275563 1.6862\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5463 1 209072.49098786863 1.2646\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5463 1 221714.34715577902 1.4754\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5463 1 232002.32697754336 1.5808\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5463 1 241041.96929957767 1.6335\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5463 1 257739.77443169686 1.6599\n",
      "I am working on tract number 5480 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5485 2 387851.706157024 1.6248\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5485 2 55238.38812697213 0.8124\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5485 2 394041.3565937084 1.6437\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5485 2 110304.08726166078 1.228\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5485 2 339748.7273369059 1.4358\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5485 2 226738.66329676064 1.3319\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5485 2 154979.57343869074 1.28\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5485 2 181300.55299497 1.306\n",
      "13 yoyos for tract,wedge,wedgePop,r= 5485 2 202091.9793811487 1.3189\n",
      "I am working on tract number 5500 of 6896 tracts\n",
      "I am working on tract number 5520 of 6896 tracts\n",
      "I am working on tract number 5540 of 6896 tracts\n",
      "I am working on tract number 5560 of 6896 tracts\n",
      "I am working on tract number 5580 of 6896 tracts\n",
      "I am working on tract number 5600 of 6896 tracts\n",
      "I am working on tract number 5620 of 6896 tracts\n",
      "I am working on tract number 5640 of 6896 tracts\n",
      "I am working on tract number 5660 of 6896 tracts\n",
      "I am working on tract number 5680 of 6896 tracts\n",
      "I am working on tract number 5700 of 6896 tracts\n",
      "I am working on tract number 5720 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5722 3 274427.77075098734 1.8647\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5722 3 117224.2409780965 0.9324\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5722 3 274561.3454823434 1.8699\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5722 3 198177.5987008231 1.4011\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5722 3 132030.61330695671 1.1667\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5722 3 137879.37557560267 1.2839\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5722 3 158060.31802516792 1.3425\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5722 3 176494.6650190123 1.3718\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5722 3 186472.80687973488 1.3865\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5723 2 236614.8482354576 2.4978\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5723 2 123075.59407524971 1.2489\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5723 2 399823.8780586929 2.7571\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5723 2 142142.66082778113 2.003\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5723 2 155356.31704316972 2.38\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5723 2 283073.4809876202 2.5686\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5723 2 217990.7814036786 2.4743\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5723 2 177100.8397942688 2.4272\n",
      "13 yoyos for tract,wedge,wedgePop,r= 5723 2 197936.21628445643 2.4507\n",
      "14 yoyos for tract,wedge,wedgePop,r= 5723 2 186355.87335218082 2.439\n",
      "I am working on tract number 5740 of 6896 tracts\n",
      "I am working on tract number 5760 of 6896 tracts\n",
      "I am working on tract number 5780 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5781 1 290763.0174338091 2.0444\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5781 1 91994.72448526055 1.0222\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5781 1 288526.60440905683 2.0163\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5781 1 131431.07555563777 1.5192\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5781 1 262099.32311158685 1.7678\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5781 1 171351.17103567973 1.6435\n",
      "13 yoyos for tract,wedge,wedgePop,r= 5781 1 233745.3146888602 1.7056\n",
      "13 yoyos for tract,wedge,wedgePop,r= 5781 1 211121.59763203346 1.6746\n",
      "14 yoyos for tract,wedge,wedgePop,r= 5781 1 191602.05806644246 1.659\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5792 0 198512.01982773596 1.5733\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5792 0 129122.53282727217 0.7867\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5792 0 229903.8393921093 1.6391\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5792 0 157390.98123529527 1.2129\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5792 0 163143.04702929984 1.426\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5792 0 171369.8715919954 1.5325\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5792 0 209190.19323150124 1.5858\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5792 0 187360.2074218685 1.5592\n",
      "13 yoyos for tract,wedge,wedgePop,r= 5792 0 197864.5103469751 1.5725\n",
      "I am working on tract number 5800 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5813 0 263652.5473261835 2.1223\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5813 0 120564.71808612213 1.0611\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5813 0 261584.1163294696 2.0991\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5813 0 153216.17978898023 1.5801\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5813 0 158381.29391460092 1.8396\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5813 0 168717.51265929118 1.9693\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5813 0 209304.10010776058 2.0342\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5813 0 181319.27650252078 2.0018\n",
      "I am working on tract number 5820 of 6896 tracts\n",
      "I am working on tract number 5840 of 6896 tracts\n",
      "I am working on tract number 5860 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5861 1 214699.26556335972 2.5537\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5861 1 116408.47276204714 1.2768\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5861 1 215606.98696424492 2.5961\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5861 1 144295.4397561499 1.9365\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5861 1 161936.89613703015 2.2663\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5861 1 199536.95776174703 2.4312\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5861 1 166272.2887331808 2.3487\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5861 1 174159.99358793965 2.39\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5861 1 187559.61421766743 2.4106\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5865 3 247051.35271494582 5.3208\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5865 3 186390.6580294941 2.6604\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5865 3 251686.88527974434 5.3305\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5865 3 196613.96287951348 3.9954\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5865 3 205164.94306936103 4.663\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5865 3 212037.98435332347 4.9967\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5865 3 218141.8573131166 5.1636\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5865 3 222692.28149900583 5.247\n",
      "I am working on tract number 5880 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5884 16.0 1 90.0 7.4883 273834.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5890 7.0 0 89.5 1.2819 193966.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5890 8.0 0 89.5 1.2708 193966.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5899 3 450733.18357297505 2.8227\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5899 3 51471.02974031976 1.4113\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5899 3 448325.14967423043 2.8171\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5899 3 374200.22042925376 2.1142\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5899 3 78613.92554391222 1.7628\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5899 3 114956.79492144773 1.9385\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5899 3 257387.15679703403 2.0264\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5899 3 174716.67394255055 1.9824\n",
      "13 yoyos for tract,wedge,wedgePop,r= 5899 3 218726.87603898934 2.0044\n",
      "13 yoyos for tract,wedge,wedgePop,r= 5899 3 197400.92638547003 1.9934\n",
      "14 yoyos for tract,wedge,wedgePop,r= 5899 3 185865.48732027717 1.9879\n",
      "I am working on tract number 5900 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5909 2 266944.71135103155 2.5303\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5909 2 31376.205573393614 1.2652\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5909 2 363693.86807863426 3.3462\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5909 2 59734.26728229853 2.3057\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5909 2 324993.25318517216 2.8259\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5909 2 288767.5752867378 2.5658\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5909 2 161174.8563773755 2.4357\n",
      "13 yoyos for tract,wedge,wedgePop,r= 5909 2 238985.1900616882 2.5008\n",
      "13 yoyos for tract,wedge,wedgePop,r= 5909 2 200660.1829515217 2.4683\n",
      "14 yoyos for tract,wedge,wedgePop,r= 5909 2 179666.86372599532 2.452\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5913 8.0 3 90.0 5.6618 209982.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5913 9.0 3 90.0 5.2845 209982.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5916 2 1305350.8345911922 7.6802\n",
      "loop31.0, tr5916,wedgePops847171.59, 189972.1, 191326.7, 193872.7, 272000.1, Overedge?0, 0, 0, 0, ,Satisfied?0100,yoyo?4032 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0035, 191739.2,0.0082, 191739.2,-0.0074, 191739.2,0.7386\n",
      "loop32.0, tr5916,wedgePops733483.34, 191464.6, 191326.7, 191777.2, 158914.8, Overedge?0, 0, 0, 0, ,Satisfied?0100,yoyo?4033 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0009, 191739.2,0.0082, 191739.2,-0.0074, 191739.2,-0.3074\n",
      "loop33.0, tr5916,wedgePops803546.28, 191464.6, 191326.7, 191777.2, 228977.8, Overedge?0, 0, 0, 0, ,Satisfied?1110,yoyo?4034 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0009, 191739.2,0.0082, 191739.2,-0.0074, 191739.2,0.0746\n",
      "loop34.0, tr5916,wedgePops775219.97, 191464.6, 191326.7, 191777.2, 200651.4, Overedge?0, 0, 0, 0, ,Satisfied?1110,yoyo?4035 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0009, 191739.2,0.0082, 191739.2,-0.0074, 191739.2,-0.0315\n",
      "loop35.0, tr5916,wedgePops766694.39, 191464.6, 191326.7, 191777.2, 192125.9, Overedge?0, 0, 0, 0, ,Satisfied?1110,yoyo?4035 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0009, 191739.2,0.0082, 191739.2,-0.0074, 191739.2,-0.008\n",
      "I am working on tract number 5920 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5922 1 426649.0519964975 3.7109\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5922 1 40066.77688099671 1.8554\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5922 1 423679.55013776827 3.6566\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5922 1 318081.2723147457 2.756\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5922 1 61681.82497656156 2.3057\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5922 1 184617.01255097683 2.5309\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5922 1 286905.43145468755 2.6435\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5922 1 237434.79577312426 2.5872\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5922 1 207937.4550570903 2.559\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5933 2 817277.6824404638 3.7042\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5933 2 389893.55017219693 1.8521\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5933 2 64088.365348936815 0.9261\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5933 2 296002.01625847176 1.7033\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5933 2 70781.4633885641 1.3147\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5933 2 81365.76906444026 1.509\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5933 2 143783.74566190323 1.6062\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5933 2 220060.71702753607 1.6548\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5933 2 177467.63442897072 1.6305\n",
      "13 yoyos for tract,wedge,wedgePop,r= 5933 2 198562.59851353194 1.6426\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5935 2.0 3 90.0 1.0783 296925.0\n",
      "I am working on tract number 5940 of 6896 tracts\n",
      "I am working on tract number 5960 of 6896 tracts\n",
      "I am working on tract number 5980 of 6896 tracts\n",
      "I am working on tract number 6000 of 6896 tracts\n",
      "I am working on tract number 6020 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6034 1 385120.0894818497 2.8652\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6034 3 556397.4697968601 2.6012\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6034 1 52002.18570872629 1.4326\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6034 3 34280.66649318795 1.3006\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6034 1 361331.88049086666 2.5753\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6034 3 554746.3891692179 2.5879\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6034 1 322358.4063487453 2.004\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6034 3 382028.0994057213 1.9443\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6034 1 136446.84314905992 1.7183\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6034 3 159457.09907147902 1.6224\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6034 1 286403.1936910656 1.8611\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6034 3 357548.02946244215 1.7834\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6034 1 219409.88697193709 1.7897\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6034 3 297966.7156679683 1.7029\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6034 1 174726.3205941436 1.754\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6034 3 239053.5410211614 1.6627\n",
      "13 yoyos for tract,wedge,wedgePop,r= 6034 1 196596.77222355752 1.7719\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6034 3 203504.83083181904 1.6426\n",
      "14 yoyos for tract,wedge,wedgePop,r= 6034 1 184627.53425692645 1.7629\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6034 3 178202.67881498515 1.6325\n",
      "I am working on tract number 6040 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6053 3 435415.1468007631 2.9504\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6053 3 52419.794718689634 1.4752\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6053 3 442232.42701882153 3.0892\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6053 3 240816.25374109662 2.2822\n",
      "loop31.0, tr6053,wedgePops650434.18, 191875.4, 191837.7, 191415.4, 75305.7, Overedge?0, 0, 0, 0, ,Satisfied?1110,yoyo?2129 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0204, 191739.2,-0.0088, 191739.2,0.0058, 191739.2,-0.4035\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6053 3 75305.70664552134 1.8787\n",
      "loop32.0, tr6053,wedgePops657869.05, 191875.4, 191837.7, 191415.4, 82740.6, Overedge?0, 0, 0, 0, ,Satisfied?1110,yoyo?21210 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0204, 191739.2,-0.0088, 191739.2,0.0058, 191739.2,0.2017\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6053 3 82740.56996616689 2.0805\n",
      "loop33.0, tr6053,wedgePops683479.14, 191875.4, 191837.7, 191415.4, 108350.7, Overedge?0, 0, 0, 0, ,Satisfied?1110,yoyo?21210 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0204, 191739.2,-0.0088, 191739.2,0.0058, 191739.2,0.1009\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6053 3 108350.6619627599 2.1813\n",
      "loop34.0, tr6053,wedgePops736887.78, 191875.4, 191837.7, 191415.4, 161759.3, Overedge?0, 0, 0, 0, ,Satisfied?1110,yoyo?21210 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0204, 191739.2,-0.0088, 191739.2,0.0058, 191739.2,0.0504\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6053 3 161759.29894543785 2.2318\n",
      "loop35.0, tr6053,wedgePops776113.47, 191875.4, 191837.7, 191415.4, 200985.0, Overedge?0, 0, 0, 0, ,Satisfied?1110,yoyo?21210 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0204, 191739.2,-0.0088, 191739.2,0.0058, 191739.2,0.0252\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6053 3 200984.99079795482 2.257\n",
      "loop36.0, tr6053,wedgePops755164.06, 191875.4, 191837.7, 191415.4, 180035.6, Overedge?0, 0, 0, 0, ,Satisfied?1110,yoyo?21211 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0204, 191739.2,-0.0088, 191739.2,0.0058, 191739.2,-0.0126\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6053 3 180035.5804294168 2.2444\n",
      "loop37.0, tr6053,wedgePops765434.16, 191875.4, 191837.7, 191415.4, 190305.7, Overedge?0, 0, 0, 0, ,Satisfied?1110,yoyo?21212 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0204, 191739.2,-0.0088, 191739.2,0.0058, 191739.2,0.0063\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6054 3 437448.8876307439 2.8147\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6054 3 43376.45903834305 1.4073\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6054 3 455497.4328706242 2.8601\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6054 3 373201.3147167222 2.1337\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6054 3 71777.76407641341 1.7705\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6054 3 131853.28652484596 1.9521\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6054 3 292219.66639474017 2.0429\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6054 3 214425.29916209035 1.9975\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6054 3 169888.679296682 1.9748\n",
      "I am working on tract number 6060 of 6896 tracts\n",
      "I am working on tract number 6080 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6087 20.0 0 100.4 7.1324 1546190.9\n",
      "I am working on tract number 6100 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6103 3 489156.4466942988 2.6993\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6103 3 67154.98576386529 1.3496\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6103 3 485003.21325607365 2.6931\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6103 3 389460.1378868937 2.0214\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6103 3 292462.4840883132 1.6855\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6103 3 85179.72040385328 1.5176\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6103 3 137364.0947298549 1.6015\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6103 3 210083.24886274192 1.6435\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6103 3 169445.8736854428 1.6225\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6106 9.0 0 90.0 5.3281 246745.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6106 3 473995.62198089133 3.1169\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6106 3 83779.99219376966 1.5584\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6106 3 473571.4443115922 3.1075\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6106 3 389595.83847309527 2.333\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6106 1 219260.30175249657 4.792\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6106 3 105651.20795784803 1.9457\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6106 1 57265.0366124025 2.396\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6106 3 128781.09942976767 2.1393\n",
      "loop31.0, tr6106,wedgePops836589.7, 191418.2, 224511.8, 191948.7, 228711.0, Overedge?0, 0, 0, 0, ,Satisfied?1010,yoyo?08510 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0052, 191739.2,2.464, 191739.2,-0.0006, 191739.2,0.0968\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6106 1 224511.84996020407 4.86\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6106 3 228710.95148237087 2.2361\n",
      "loop32.0, tr6106,wedgePops622151.4, 191418.2, 85079.5, 191948.7, 153705.0, Overedge?0, 0, 0, 0, ,Satisfied?1010,yoyo?09511 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0052, 191739.2,-1.232, 191739.2,-0.0006, 191739.2,-0.0484\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6106 1 85079.48595164643 3.628\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6106 3 153705.02050913882 2.1877\n",
      "loop33.0, tr6106,wedgePops725804.33, 191418.2, 152430.7, 191948.7, 190006.8, Overedge?0, 0, 0, 0, ,Satisfied?1010,yoyo?010512 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0052, 191739.2,0.616, 191739.2,-0.0006, 191739.2,0.0242\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6106 1 152430.66204490775 4.244\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6106 3 190006.7713503843 2.2119\n",
      "loop34.0, tr6106,wedgePops758037.71, 191418.2, 166664.0, 191948.7, 208006.8, Overedge?0, 0, 0, 0, ,Satisfied?1010,yoyo?010512 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0052, 191739.2,0.308, 191739.2,-0.0006, 191739.2,0.0121\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6106 1 166663.9789194224 4.552\n",
      "13 yoyos for tract,wedge,wedgePop,r= 6106 3 208006.8339680536 2.224\n",
      "loop35.0, tr6106,wedgePops798542.12, 191418.2, 216181.3, 191948.7, 198993.9, Overedge?0, 0, 0, 0, ,Satisfied?1010,yoyo?010513 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0052, 191739.2,0.154, 191739.2,-0.0006, 191739.2,-0.0061\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6106 1 216181.33371934353 4.706\n",
      "13 yoyos for tract,wedge,wedgePop,r= 6106 3 198993.88632553056 2.218\n",
      "loop36.0, tr6106,wedgePops781590.69, 191418.2, 203536.5, 191948.7, 194687.2, Overedge?0, 0, 0, 0, ,Satisfied?1010,yoyo?011513 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0052, 191739.2,-0.077, 191739.2,-0.0006, 191739.2,-0.003\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6106 1 203536.5426427885 4.629\n",
      "13 yoyos for tract,wedge,wedgePop,r= 6106 3 194687.2494744601 2.215\n",
      "loop37.0, tr6106,wedgePops758561.43, 191418.2, 182714.7, 191948.7, 192479.8, Overedge?0, 0, 0, 0, ,Satisfied?1010,yoyo?011513 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0052, 191739.2,-0.0385, 191739.2,-0.0006, 191739.2,-0.0015\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6106 1 182714.70969573403 4.5905\n",
      "13 yoyos for tract,wedge,wedgePop,r= 6106 3 192479.82533924768 2.2135\n",
      "loop38.0, tr6106,wedgePops768408.56, 191418.2, 193777.1, 191948.7, 191264.6, Overedge?0, 0, 0, 0, ,Satisfied?1010,yoyo?012513 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0052, 191739.2,0.0193, 191739.2,-0.0006, 191739.2,-0.0008\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6118 3 556081.1068466528 2.7359\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6118 3 17972.755053030327 1.368\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6118 3 557889.7028830816 2.7515\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6118 3 45089.76076750958 2.0598\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6118 3 130224.34094408587 2.4057\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6118 3 490284.0035155564 2.5786\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6118 3 257883.87216645008 2.4921\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6118 3 158952.68008118396 2.4489\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6118 3 193505.1286243226 2.4705\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6118 3 218214.90435859267 2.4813\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6118 3 237161.5732040946 2.4867\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6118 3 247285.63025280344 2.4894\n",
      "I am working on tract number 6120 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6122 3.0 2 90.0 0.8755 160287.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6122 8.0 0 107.4 0.8241 208453.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6122 13.0 0 107.4 0.8239 208453.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6122 18.0 0 107.4 0.821 208453.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6122 0 206438.82856864596 0.7777\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6122 0 68681.27911526078 0.3889\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6122 0 208453.73404981868 0.8185\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6122 0 151868.4383405013 0.6037\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6122 0 162430.14401443274 0.7111\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6138 3 203787.30779827057 2.1577\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6138 3 60942.535658926994 1.0789\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6138 3 231752.09000126217 2.4816\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6138 3 81875.0982387806 1.7802\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6139 1 377630.5958067509 2.9058\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6139 1 53107.208253254124 1.4529\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6139 1 378528.3478672155 2.9217\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6139 1 89988.64387979085 2.1873\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6139 1 152660.834924892 2.5545\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6139 1 357902.4842472648 2.7381\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6139 1 266743.35520507244 2.6463\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6139 1 195823.57011343352 2.6004\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6139 1 169554.30827211848 2.5775\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6139 1 181470.6251436185 2.5889\n",
      "I am working on tract number 6140 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6145 3 602489.3958174901 3.7057\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6145 3 517777.30984659196 1.9603\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6145 3 244352.09149582864 1.0876\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6145 3 259757.75183137058 1.5239\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6145 3 454260.87838927284 1.7421\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6153 8.0 3 90.0 0.7741 200633.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6153 9.0 3 90.0 0.7481 200633.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6153 3 200264.65256085963 0.687\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6153 3 62124.695973805414 0.3435\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6153 3 200388.17538523892 0.6952\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6153 3 137723.8114398922 0.5193\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6153 3 148508.68407642972 0.6072\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6153 3 168704.80910458334 0.6512\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6153 3 198662.12205827513 0.6732\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6153 3 186964.04084300657 0.6622\n",
      "I am working on tract number 6160 of 6896 tracts\n",
      "I am working on tract number 6180 of 6896 tracts\n",
      "I am working on tract number 6200 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6217 2 478851.0119527134 2.6485\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6217 2 27197.20673530636 1.3243\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6217 2 493630.50643260905 2.7389\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6217 2 371854.5896746718 2.0316\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6217 2 61188.70219179359 1.6779\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6217 2 271851.38968678506 1.8547\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6217 2 110506.51935040826 1.7663\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6217 2 180565.77021450436 1.8105\n",
      "13 yoyos for tract,wedge,wedgePop,r= 6217 2 223423.3107471645 1.8326\n",
      "13 yoyos for tract,wedge,wedgePop,r= 6217 2 201739.2292973714 1.8216\n",
      "I am working on tract number 6220 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6223 1 277557.70631208044 1.8977\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6223 1 146414.34063861318 0.9489\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6223 1 297601.4081833933 2.2463\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6223 1 164170.84732726557 1.5976\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6223 1 280823.9394776693 1.9219\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6223 1 243541.13531007402 1.7597\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6223 1 183203.59962590385 1.6787\n",
      "13 yoyos for tract,wedge,wedgePop,r= 6223 1 222992.82934302636 1.7192\n",
      "13 yoyos for tract,wedge,wedgePop,r= 6223 1 203684.1667525792 1.6989\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6228 0 778191.2602291885 4.0682\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6228 0 369920.6201519847 2.0341\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6228 0 118367.74307184078 1.0171\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6228 0 324652.52146032127 1.8765\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6228 0 123335.19570009303 1.4468\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6228 0 133013.762737923 1.6617\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6228 0 225950.40340220288 1.7691\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6228 0 153137.25967954623 1.7154\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6228 0 185508.17818337772 1.7422\n",
      "13 yoyos for tract,wedge,wedgePop,r= 6228 0 204847.97534958596 1.7557\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6231 10.0 0 90.0 6.8982 249522.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6231 2 245267.46287965897 2.4466\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6231 2 120501.04122088564 1.2233\n",
      "loop31.0, tr6231,wedgePops796290.3, 118973.2, 216192.4, 245306.9, 215817.8, Overedge?1, 0, 0, 0, ,Satisfied?0101,yoyo?0582 \n",
      "   targetWP, latest drx4 are tWP,dr, 215994.5,1.2776, 215994.5,-0.0007, 215994.5,1.2237, 215994.5,0.0019\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6231 2 245306.93056711974 2.447\n",
      "loop32.0, tr6231,wedgePops706915.99, 118973.2, 216192.4, 155932.6, 215817.8, Overedge?1, 0, 0, 0, ,Satisfied?0101,yoyo?0592 \n",
      "   targetWP, latest drx4 are tWP,dr, 215994.5,1.2776, 215994.5,-0.0007, 215994.5,-0.6119, 215994.5,0.0019\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6231 2 155932.61355852836 1.8351\n",
      "loop33.0, tr6231,wedgePops737469.59, 118973.2, 216192.4, 186486.2, 215817.8, Overedge?1, 0, 0, 0, ,Satisfied?0101,yoyo?05102 \n",
      "   targetWP, latest drx4 are tWP,dr, 215994.5,1.2776, 215994.5,-0.0007, 215994.5,0.3059, 215994.5,0.0019\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6231 2 186486.22274063795 2.1411\n",
      "loop34.0, tr6231,wedgePops768071.91, 118973.2, 216192.4, 217088.5, 215817.8, Overedge?1, 0, 0, 0, ,Satisfied?0101,yoyo?05102 \n",
      "   targetWP, latest drx4 are tWP,dr, 215994.5,1.2776, 215994.5,-0.0007, 215994.5,0.153, 215994.5,0.0019\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6233 2 523474.6223981094 2.2799\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6233 2 114551.4016312774 1.14\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6233 2 510183.408135422 2.1983\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6233 2 126761.26060424966 1.6691\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6233 2 381286.13195322873 1.9337\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6233 2 147634.9246125965 1.8014\n",
      "13 yoyos for tract,wedge,wedgePop,r= 6233 2 219397.54435160733 1.8675\n",
      "14 yoyos for tract,wedge,wedgePop,r= 6233 2 167896.53879578333 1.8345\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6235 1 560826.3279219402 2.5379\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6235 1 95968.8407919748 1.2689\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6235 1 519811.84006961685 2.4002\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6235 1 129428.08835448907 1.8346\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6235 1 487443.4420210194 2.1174\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6235 1 381828.31291967945 1.976\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6235 1 207448.4446206749 1.9053\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6235 1 150574.80955296417 1.8699\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6235 1 176709.82396680987 1.8876\n",
      "I am working on tract number 6240 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6249 2 764527.0217416019 4.0829\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6249 2 359413.0506042647 2.0415\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6249 2 86068.08809355699 1.0207\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6249 2 326267.7487069679 1.9074\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6249 2 90908.55517938104 1.4641\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6249 2 107052.51629130467 1.6857\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6249 2 258703.2733740177 1.7966\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6249 2 159274.98951857572 1.7412\n",
      "13 yoyos for tract,wedge,wedgePop,r= 6249 2 201989.10375274194 1.7689\n",
      "14 yoyos for tract,wedge,wedgePop,r= 6249 2 179653.3916637067 1.755\n",
      "I am working on tract number 6260 of 6896 tracts\n",
      "I am working on tract number 6280 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 6299\n",
      "I am working on tract number 6300 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6315 2 474165.0601955411 3.1705\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6315 2 60528.81512739934 1.5853\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6315 2 567468.2701561145 3.7358\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6315 2 127578.86424034758 2.6606\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6315 2 475968.4680974895 3.1982\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6315 2 435836.6896594011 2.9294\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6315 2 219415.58618914383 2.795\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6315 2 155843.8621792205 2.7278\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6315 2 183996.7712311589 2.7614\n",
      "13 yoyos for tract,wedge,wedgePop,r= 6315 2 201868.62013748963 2.7782\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6318 5.0 1 90.0 2.1453 196273.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6318 6.0 1 90.0 2.108 196273.5\n",
      "I am working on tract number 6320 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6320 6.0 0 124.6 2.6044 199328.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6320 7.0 0 124.6 2.5293 199328.7\n",
      "I am working on tract number 6340 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6346 3 715045.6171125094 3.2632\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6346 3 34095.71806800226 1.6316\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6346 3 720169.0379526527 3.3171\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6346 3 589577.4641857846 2.4743\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6353 3 348888.7370149242 3.2042\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6353 3 39143.28839171218 1.6021\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6353 3 348889.47673384397 3.2042\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6353 3 136751.4821541017 2.4032\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6353 3 310998.1535205019 2.8037\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6353 3 270212.2712188631 2.6034\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6356 0 803744.67046569 3.6793\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6356 0 95297.18997309962 1.8397\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6356 0 799705.2929155541 3.6362\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6356 0 631296.0433560965 2.7379\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6356 0 473914.39101669396 2.2888\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6356 0 443990.9373509484 2.0642\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6356 0 295965.21288919146 1.9519\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6356 0 165654.246784226 1.8958\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6356 0 231201.09860745014 1.9239\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6356 0 197836.08078982908 1.9098\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6356 0 181411.43728591673 1.9028\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6359 1 858658.5627950155 4.1222\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6359 1 430481.78495776124 2.0611\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6359 1 44895.412500950915 1.0306\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6359 1 260252.90137191728 1.8654\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6359 1 50989.40627829044 1.448\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6359 1 64350.205140495986 1.6567\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6359 1 84216.0306994571 1.7611\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6359 1 144568.45379883033 1.8133\n",
      "I am working on tract number 6360 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6369 0 198768.28918334973 0.7951\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6369 0 179028.2197457034 0.3976\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6369 0 198769.18420403544 0.7952\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6369 0 183508.66139432893 0.5964\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6369 0 197011.56557562735 0.6958\n",
      "I am working on tract number 6380 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6398 4.0 0 90.0 1.7356 311714.9\n",
      "we have 2 non-opposing shorted wedges for tract no 6398\n",
      "I am working on tract number 6400 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6402 9.0 0 90.0 6.9736 220131.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6406 11.0 3 83.8 1.7261 383131.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6408 3 301628.8873913501 2.5368\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6408 3 81008.4075775668 1.2684\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6408 3 301765.0747265355 2.5477\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6408 3 230698.95269817827 1.908\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6408 3 99182.2658934641 1.5882\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6408 3 126638.08354051379 1.7481\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6408 3 152404.28632430034 1.8281\n",
      "loop31.0, tr6410,wedgePops796659.45, 96220.7, 224032.0, 252514.2, 223892.5, Overedge?1, 0, 0, 0, ,Satisfied?0101,yoyo?0165 \n",
      "   targetWP, latest drx4 are tWP,dr, 223578.7,1.2776, 223578.7,-0.0044, 223578.7,0.2189, 223578.7,-0.0015\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6410 2 252514.15340873963 2.5274\n",
      "loop32.0, tr6410,wedgePops664510.92, 96220.7, 224032.0, 120365.6, 223892.5, Overedge?1, 0, 0, 0, ,Satisfied?0101,yoyo?0175 \n",
      "   targetWP, latest drx4 are tWP,dr, 223578.7,1.2776, 223578.7,-0.0044, 223578.7,-1.2637, 223578.7,-0.0015\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6410 2 120365.62251499385 1.2637\n",
      "loop33.0, tr6410,wedgePops796444.51, 96220.7, 224032.0, 252299.2, 223892.5, Overedge?1, 0, 0, 0, ,Satisfied?0101,yoyo?0185 \n",
      "   targetWP, latest drx4 are tWP,dr, 223578.7,1.2776, 223578.7,-0.0044, 223578.7,1.2613, 223578.7,-0.0015\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6410 2 252299.2188938397 2.525\n",
      "loop34.0, tr6410,wedgePops706903.88, 96220.7, 224032.0, 162758.6, 223892.5, Overedge?1, 0, 0, 0, ,Satisfied?0101,yoyo?0195 \n",
      "   targetWP, latest drx4 are tWP,dr, 223578.7,1.2776, 223578.7,-0.0044, 223578.7,-0.6307, 223578.7,-0.0015\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6410 2 162758.58089979808 1.8944\n",
      "loop35.0, tr6410,wedgePops737502.92, 96220.7, 224032.0, 193357.6, 223892.5, Overedge?1, 0, 0, 0, ,Satisfied?0101,yoyo?01105 \n",
      "   targetWP, latest drx4 are tWP,dr, 223578.7,1.2776, 223578.7,-0.0044, 223578.7,0.3153, 223578.7,-0.0015\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6410 2 193357.6213590608 2.2097\n",
      "loop36.0, tr6410,wedgePops762960.14, 96220.7, 224032.0, 218814.8, 223892.5, Overedge?1, 0, 0, 0, ,Satisfied?0101,yoyo?01105 \n",
      "   targetWP, latest drx4 are tWP,dr, 223578.7,1.2776, 223578.7,-0.0044, 223578.7,0.1577, 223578.7,-0.0015\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6410 2 218814.84157673072 2.3674\n",
      "loop37.0, tr6410,wedgePops790210.36, 96220.7, 224032.0, 246065.1, 223892.5, Overedge?1, 0, 0, 0, ,Satisfied?0101,yoyo?01105 \n",
      "   targetWP, latest drx4 are tWP,dr, 223578.7,1.2776, 223578.7,-0.0044, 223578.7,0.0788, 223578.7,-0.0015\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6410 2 246065.06412193956 2.4462\n",
      "loop38.0, tr6410,wedgePops786025.47, 96220.7, 224032.0, 241880.2, 223892.5, Overedge?1, 0, 0, 0, ,Satisfied?0101,yoyo?01115 \n",
      "   targetWP, latest drx4 are tWP,dr, 223578.7,1.2776, 223578.7,-0.0044, 223578.7,-0.0394, 223578.7,-0.0015\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6410 2 241880.17601828199 2.4068\n",
      "loop39.0, tr6410,wedgePops776955.28, 96220.7, 224032.0, 232810.0, 223892.5, Overedge?1, 0, 0, 0, ,Satisfied?0101,yoyo?01115 \n",
      "   targetWP, latest drx4 are tWP,dr, 223578.7,1.2776, 223578.7,-0.0044, 223578.7,-0.0197, 223578.7,-0.0015\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.66874 26758.4736 96220.7448 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 1.7091 224233.6785 224032.0376 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 2.38709 241880.176 232809.9811 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.36651 225125.6722 223892.5121 0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 yoyos for tract,wedge,wedgePop,r= 6410 2 232809.98105798298 2.3871\n",
      "loop40.0, tr6410,wedgePops769959.24, 96220.7, 224032.0, 225813.9, 223892.5, Overedge?1, 0, 0, 0, ,Satisfied?0101,yoyo?01115 \n",
      "   targetWP, latest drx4 are tWP,dr, 223578.7,1.2776, 223578.7,-0.0044, 223578.7,-0.0099, 223578.7,-0.0015\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.66874 26758.4736 96220.7448 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 1.7091 224233.6785 224032.0376 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 2.37723 232809.9811 225813.9422 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.36651 225125.6722 223892.5121 0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 6410, giving up w pop 769959.2366749761\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6411 2 243449.21205933415 1.9722\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6411 2 42667.810849563364 0.9861\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6411 2 269909.36347253656 2.2719\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6411 2 77679.09288534388 1.629\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6411 2 238600.1225610609 1.9504\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6411 2 133918.1677348531 1.7897\n",
      "13 yoyos for tract,wedge,wedgePop,r= 6411 2 210610.106624819 1.8701\n",
      "14 yoyos for tract,wedge,wedgePop,r= 6411 2 176177.73408247746 1.8299\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6413 8.0 3 90.0 6.7959 213904.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6413 14.0 3 90.0 6.7385 213904.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6413 19.0 3 90.0 9.7624 213904.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6413 20.0 3 90.0 8.983 213904.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6413 21.0 3 90.0 8.2658 213904.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6413 22.0 3 90.0 7.6058 213904.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6413 23.0 3 90.0 6.9985 213904.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6413 24.0 3 90.0 6.4398 213904.6\n",
      "loop31.0, tr6413,wedgePops714861.91, 187700.1, 149971.2, 188580.4, 188610.3, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0222 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,1.2776, 191739.2,0.1268, 191739.2,0.0869, 191739.2,0.006\n",
      "loop32.0, tr6413,wedgePops813937.3, 196863.3, 234240.4, 191064.7, 191768.9, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0222 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,0.0421, 191739.2,0.0653, 191739.2,0.0188, 191739.2,0.0014\n",
      "loop33.0, tr6413,wedgePops783283.8, 194208.0, 205697.5, 191609.4, 191768.9, Overedge?0, 0, 0, 0, ,Satisfied?0001,yoyo?1322 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,-0.0188, 191739.2,-0.0261, 191739.2,0.0041, 191739.2,0.0014\n",
      "loop34.0, tr6413,wedgePops767129.53, 190886.3, 192864.9, 191609.4, 191768.9, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 192023.5,-0.014, 192023.5,-0.0103, 192023.5,0.0041, 192023.5,0.0014\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6419 9.0 2 90.0 6.8464 242568.3\n",
      "I am working on tract number 6420 of 6896 tracts\n",
      "I am working on tract number 6440 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6448 3 454567.3284586617 3.8039\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6448 3 162582.85237571248 1.9019\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6448 3 452877.00988516165 3.7432\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6448 3 427063.3039864432 2.8226\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6448 3 425651.74862294405 2.3622\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6448 3 398109.1122384611 2.1321\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6448 3 192289.483709589 2.017\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6448 3 291280.9927450492 2.0745\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6448 3 244314.93622285983 2.0458\n",
      "loop31.0, tr6448,wedgePops786760.22, 177564.4, 196287.1, 196770.0, 216138.8, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?00311 \n",
      "   targetWP, latest drx4 are tWP,dr, 196464.2,0.0455, 196464.2,0.0004, 196464.2,-0.0032, 196464.2,-0.0144\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6448 3 216138.7833526338 2.0314\n",
      "loop32.0, tr6448,wedgePops773561.53, 177564.4, 196287.1, 196770.0, 202940.1, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?00311 \n",
      "   targetWP, latest drx4 are tWP,dr, 196464.2,0.0455, 196464.2,0.0004, 196464.2,-0.0032, 196464.2,-0.0072\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6448 3 202940.0897941838 2.0242\n",
      "loop33.0, tr6448,wedgePops767994.79, 177564.4, 196287.1, 196770.0, 197373.4, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?00311 \n",
      "   targetWP, latest drx4 are tWP,dr, 196464.2,0.0455, 196464.2,0.0004, 196464.2,-0.0032, 196464.2,-0.0036\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6453 0 316976.7799334207 0.5723\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6453 0 11537.837547510804 0.2862\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6453 0 424907.1930219964 0.739\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6453 0 144950.91786904115 0.5126\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6453 0 400426.9697417036 0.6258\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6453 0 308820.24498884013 0.5692\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6453 0 232951.41845089072 0.5409\n",
      "I am working on tract number 6460 of 6896 tracts\n",
      "I am working on tract number 6480 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6489 1 620734.8603483067 3.0018\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6489 1 95959.36419998028 1.5009\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6489 1 632857.9261941834 3.121\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6489 1 506597.6446817632 2.3109\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6489 1 449164.14547906234 1.9059\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6489 1 399825.6065238213 1.7034\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6489 1 109338.7560124863 1.6022\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6489 1 253091.07456904434 1.6528\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6489 1 341384.2943499902 1.6781\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6489 1 380851.9884638493 1.6908\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6489 1 361228.15879273065 1.6844\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6494 0 203672.5324891749 3.162\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6494 0 72438.8023134227 1.581\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6494 0 203678.24465834754 3.1622\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6494 0 103613.1246220759 2.3716\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6494 0 158907.33846649964 2.7669\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6494 0 167451.6069864262 2.9646\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6494 0 200338.69175718125 3.0634\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6494 0 181940.1529121638 3.014\n",
      "I am working on tract number 6500 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6507 0 391207.35728402936 2.8346\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6507 0 76885.56574342947 1.4173\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6507 0 338251.60397543944 2.6118\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6507 0 320465.44491741573 2.0145\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6507 0 97567.55798197401 1.7159\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6507 0 125141.83102995355 1.8652\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6507 0 239607.27969737016 1.9399\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6507 0 155243.99148287473 1.9026\n",
      "I am working on tract number 6520 of 6896 tracts\n",
      "I am working on tract number 6540 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 6543\n",
      "we have 2 non-opposing shorted wedges for tract no 6551\n",
      "I am working on tract number 6560 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 6564\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6573 3 910018.2875828181 1.9879\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6573 3 32849.55279229989 0.9939\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6573 3 943669.9225207345 2.1927\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6573 3 781425.3952417569 1.5933\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6573 3 615098.723788907 1.2936\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6573 3 245635.42532360874 1.1438\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6573 3 71388.5380817379 1.0689\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6573 3 137809.76308174146 1.1063\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6573 3 189408.61811022455 1.1251\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6573 3 216248.73089145077 1.1344\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6573 3 202401.0696092262 1.1297\n",
      "I am working on tract number 6580 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6580 3 526029.028049336 3.4588\n",
      "we have 2 non-opposing shorted wedges for tract no 6581\n",
      "we have 2 non-opposing shorted wedges for tract no 6582\n",
      "we have 2 non-opposing shorted wedges for tract no 6595\n",
      "I am working on tract number 6600 of 6896 tracts\n",
      "I am working on tract number 6620 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6633 5.0 3 72.1 2.3964 315610.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6634 1 349077.9513852586 1.7712\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6634 1 19297.706619871547 0.8856\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6634 1 353335.39362538414 1.8492\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6634 1 37901.67764364224 1.3674\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6634 1 220711.8926593122 1.6083\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6634 1 343037.637797749 1.7288\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6634 1 300403.9800540509 1.6685\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6634 1 262014.46738259983 1.6384\n",
      "13 yoyos for tract,wedge,wedgePop,r= 6634 1 282347.10932583595 1.6535\n",
      "14 yoyos for tract,wedge,wedgePop,r= 6634 1 272189.7567454721 1.646\n",
      "I am working on tract number 6640 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6649 2.0 0 90.0 1.1865 199162.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6649 3.0 0 90.0 1.153 199162.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6649 4.0 0 90.0 1.1205 199162.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6649 5.0 0 90.0 1.0889 199162.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6649 6.0 0 90.0 1.0581 199162.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6649 7.0 0 90.0 1.0283 199162.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6649 8.0 0 90.0 0.9993 199162.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6649 9.0 0 90.0 0.9711 199162.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6649 10.0 0 90.0 0.9436 199162.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6659 3 283671.4325925994 1.749\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6659 3 4023.368288337253 0.8745\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6659 3 292759.8440509495 2.2048\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6659 3 282668.9299916228 1.5397\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6659 3 21226.811293661973 1.2071\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6659 3 186327.39950874366 1.3734\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6659 3 279699.8879944053 1.4565\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6659 3 263935.36942178675 1.4149\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6659 3 237990.63824291326 1.3942\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6659 3 214582.8681030765 1.3838\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6659 3 200828.35468953522 1.3786\n",
      "I am working on tract number 6660 of 6896 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 6669\n",
      "we have 2 non-opposing shorted wedges for tract no 6670\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6673 3 463843.1585650085 1.1228\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6673 3 77901.73794213892 0.5614\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6673 3 463924.6355974711 1.1233\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6673 3 413078.8590168326 0.8424\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6673 3 155015.14442098245 0.7019\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6673 3 282007.6215327954 0.7721\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6673 3 217499.81581272394 0.737\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6673 3 182407.41026089378 0.7195\n",
      "13 yoyos for tract,wedge,wedgePop,r= 6673 3 202744.75398065636 0.7282\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6677 1 433049.29700701346 3.1796\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6678 3 459386.7132843314 3.4418\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6679 3 439035.0083276709 3.268\n",
      "loop31.0, tr6679,wedgePops1010617.93, 435952.5, 191567.8, 191308.1, 191789.6, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6243 \n",
      "   targetWP, latest drx4 are tWP,dr, 191739.2,1.3996, 191739.2,0.0006, 191739.2,0.0004, 191739.2,-0.0048\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6679 0 435952.50986698654 3.2913\n",
      "loop32.0, tr6679,wedgePops635112.42, 60447.0, 191567.8, 191308.1, 191789.6, Overedge?1, 0, 0, 0, ,Satisfied?0111,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 235503.3,-1.6456, 235503.3,0.0006, 235503.3,0.0004, 235503.3,-0.0048\n",
      "loop33.0, tr6679,wedgePops707957.2, 60447.0, 215027.7, 228990.3, 203492.2, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0001 \n",
      "   targetWP, latest drx4 are tWP,dr, 235503.3,-1.6456, 235503.3,0.0381, 235503.3,0.0112, 235503.3,0.2262\n",
      "loop34.0, tr6679,wedgePops763331.85, 60447.0, 224977.6, 233970.1, 243937.2, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0001 \n",
      "   targetWP, latest drx4 are tWP,dr, 235503.3,-1.6456, 235503.3,0.0261, 235503.3,0.0015, 235503.3,0.4247\n",
      "I am working on tract number 6680 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6685 12.0 1 90.0 6.7433 221065.9\n",
      "we have 2 non-opposing shorted wedges for tract no 6688\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6693 3 324696.5108619607 2.7482\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6693 3 134621.0983196137 1.3741\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6693 3 324685.2938535555 2.7457\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6693 3 324256.9526134223 2.0599\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6693 3 237337.86122112744 1.717\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6693 3 135170.72489733418 1.5456\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6693 3 137145.1950477308 1.6313\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6693 3 156389.94242242968 1.6741\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6693 3 198448.09844727468 1.6956\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6693 3 220634.66058888132 1.7063\n",
      "I am working on tract number 6700 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6703 0 364937.9557077516 2.5257\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6703 0 13074.240803780442 1.2629\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6703 0 364930.6670227227 2.5251\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6703 0 163878.2621544596 1.894\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6703 0 359184.9996543175 2.2095\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6703 0 327756.23551695555 2.0517\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6703 0 261309.65640691848 1.9728\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6703 0 199229.1596760425 1.9334\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6703 0 177164.40085416762 1.9137\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6703 0 186148.64669032604 1.9235\n",
      "I am working on tract number 6720 of 6896 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6727 15.0 0 138.6 3.2844 270132.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6727 0 270132.69809664495 2.9248\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6733 2.0 3 90.0 1.1658 251000.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6733 11.0 3 90.0 1.1861 251000.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6733 3 251000.7610508332 1.3277\n",
      "I am working on tract number 6740 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6740 0 357549.07063586183 3.0245\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6740 0 23516.482018462557 1.5123\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6740 0 355853.1361251187 2.9645\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6740 0 304593.0410131776 2.2384\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6740 0 49693.89446571798 1.8753\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6740 0 211967.4845724177 2.0568\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6740 0 84395.07776181663 1.9661\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6740 0 126031.85964064364 2.0115\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6740 0 168782.81859617267 2.0342\n",
      "we have 2 non-opposing shorted wedges for tract no 6744\n",
      "I am working on tract number 6760 of 6896 tracts\n",
      "I am working on tract number 6780 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6797 3 428079.5082402441 2.0298\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6797 3 32372.23877922719 1.0149\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6797 3 428079.508240244 2.0286\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6797 3 221219.57855647232 1.5217\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6797 3 424319.4736985701 1.7752\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6797 3 324858.7288396195 1.6485\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6797 3 275917.95509021805 1.5851\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6797 3 254146.05803221092 1.5534\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6797 3 237919.2332306195 1.5376\n",
      "we have 2 non-opposing shorted wedges for tract no 6799\n",
      "I am working on tract number 6800 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6802 1 214945.54987140643 2.2565\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6802 1 63205.868438747915 1.1282\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6802 2 429002.61440422706 2.5451\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6802 1 222565.55332307186 2.4078\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6802 2 53391.323482136184 1.2726\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6802 1 84388.2405162348 1.768\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6802 2 433958.33324395784 2.6308\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6802 1 124739.944202098 2.0879\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6802 2 99491.75263876561 1.9517\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6802 1 213348.96243743302 2.2478\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6802 2 409664.0846289303 2.2912\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6802 1 181958.67188838992 2.1679\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6802 2 248773.80035356345 2.1215\n",
      "13 yoyos for tract,wedge,wedgePop,r= 6802 1 199200.25465601624 2.2079\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6802 2 119811.25320233883 2.0366\n",
      "13 yoyos for tract,wedge,wedgePop,r= 6802 1 192923.23068768624 2.1879\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6802 2 172796.86219484484 2.079\n",
      "14 yoyos for tract,wedge,wedgePop,r= 6802 1 188184.13506738178 2.1779\n",
      "13 yoyos for tract,wedge,wedgePop,r= 6802 2 211783.5131587698 2.1002\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 6816 19.0 0 104.4 2.2584 298278.7\n",
      "we have 2 non-opposing shorted wedges for tract no 6817\n",
      "I am working on tract number 6820 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6838 1 272388.8947756886 2.7199\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6838 1 114008.49696747167 1.3599\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6838 1 269708.6896620469 2.699\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6838 1 230956.4701590247 2.0295\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6838 1 124092.46778678778 1.6947\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6838 1 152972.62178657594 1.8621\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6838 1 208538.19208936798 1.9458\n",
      "I am working on tract number 6840 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6856 0 243945.61498826358 1.9621\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6856 0 113686.17487861136 0.9811\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6856 0 243951.3471547304 1.9624\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6856 0 148263.33558846498 1.4717\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6856 0 233774.32754816936 1.7171\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6856 0 215864.55641715165 1.5944\n",
      "I am working on tract number 6860 of 6896 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6867 3 196248.18296354832 1.6245\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6867 3 41934.14960817227 0.8123\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6867 3 309652.87296173186 1.6784\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6867 3 94262.54068649234 1.2453\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6867 3 127358.69084246777 1.4619\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6867 3 151449.1668007431 1.5701\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6867 3 195892.20067342953 1.6243\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6867 3 166405.14808483634 1.5972\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6867 3 179622.7297014342 1.6107\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6867 3 187339.5736162754 1.6175\n",
      "7 yoyos for tract,wedge,wedgePop,r= 6871 0 396217.23195539485 1.5793\n",
      "8 yoyos for tract,wedge,wedgePop,r= 6871 0 34177.96550270257 0.7896\n",
      "9 yoyos for tract,wedge,wedgePop,r= 6871 0 387855.39120359055 1.5591\n",
      "10 yoyos for tract,wedge,wedgePop,r= 6871 0 131338.06285254558 1.1744\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6871 0 330661.9967371045 1.3667\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6871 0 278142.54829086305 1.2706\n",
      "11 yoyos for tract,wedge,wedgePop,r= 6871 0 205901.69142605108 1.2225\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6871 0 160310.21529616672 1.1984\n",
      "12 yoyos for tract,wedge,wedgePop,r= 6871 0 182889.38802893454 1.2104\n",
      "I am working on tract number 6880 of 6896 tracts\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.0003963437525978\n",
      "Average and max number of wedgePop loops per tract were:  7.257395591647332 40.0\n",
      "min,average,max of Home District areas were:  0.0 1.824736609749439 17.514238016990454\n",
      "calculated statewide vote was 0.5347060037543987, should have been 0.5283093020704941\n",
      "calcd statewide Hispanic pop was 0.36186812577679833, should have been 0.3402898928507731\n",
      "calcd statewide Black pop was 0.11933074327778365, should have been 0.11927730293094066\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.16908352668213458\n"
     ]
    }
   ],
   "source": [
    "#3/13/22 - from HD2, but using coeff1, coeff2 (unrestricted opp wedge pop, wideAngle quadratic in ratio)\n",
    "#  and 3/23/22 min of 5pct wedge on map to continue growth\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "coeff1 = 0.3  #linear term. we will adjust this empirically to tighten tract weighting near boundaries\n",
    "coeff2 = 0.3  #quadratic term.  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    if isEmptyGrid[nG] == 0: #NEW 3/18/22 TO SAVE A LITTLE TIME, skip empty grids\n",
    "                        gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                        if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                            for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                                nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                                if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                    usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                    overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                    if overlap > 0 :\n",
    "                                        fracArea = overlap/tractArea[nTT]\n",
    "                                        latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                        loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                            # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])  #leading edge of growing wedgePoly\n",
    "                    fracArea = wedgePoly.intersection(MAP).area / max(0.00000001,wedgePoly.area) #what pct of new wedge piece on map?\n",
    "                    if leadingEdge.intersects(MAP) and fracArea > 0.05 :  #still room to grow in part of edge...\n",
    "                        conclusion = \"keep going\"  #** NEW 3/23/22 - add fracArea criterion on top of leadingEdge criterion *****\n",
    "                    else: #conclude this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "\n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if 0 == 1: #always false; for HD2 we would have checked here for opp wedge restriction; ignore this block\n",
    "            # targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opp wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding flagged opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # assign new wedge angles.  The two wide angles face toward and away from nearest boundary\n",
    "                #ratio = wedgePopGap[shortW]/ (avgDistrictPop/nWedges)  #0-1; this is degree of shortness\n",
    "                ratio = 1. - minWedgePop[t]/ (avgDistrictPop/nWedges)  # *** UPDATED\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                wideAngle = (1. + coeff1 * ratio + coeff2 * ratio*ratio)*avgWedgeAngle #HERE, WE ADJUST ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                #if (t % 9 == 0):  #occasional print\n",
    "                    #print(\"tract,wide, thin angles are \",t,round(180/pi*wideAngle,4),round(180/pi*thinAngle,4) )\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                targetWedgePop = [avgDistrictPop/nWedges] * nWedges\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.0003963437525978\n",
      "Average and max number of wedgePop loops per tract were:  7.257395591647332 40.0\n",
      "min,average,max of Home District areas were:  0.0 1.824736609749439 17.514238016990454 for  TX\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start TX 10:43pm, end 4:46am\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TX 24MarnoElP\n"
     ]
    }
   ],
   "source": [
    "date = \"24MarnoElP\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"coeff1\",\"coeff2\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, coeff1,coeff2]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"tractVAP\":tractVAP,\"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4318 0.97 ( -102.33620761632056 31.868816375681483 ) 0.7254152410921397 t, pop/target,(x,y), pctR\n",
      "5447 1.0 ( -98.14201659899383 27.223508071968844 ) 0.46266485534459745 t, pop/target,(x,y), pctR\n",
      "6410 1.0 ( -101.92642024775846 33.60254169145064 ) 0.7467307287174421 t, pop/target,(x,y), pctR\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [0]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "249 0.7856816029997915 971 pop,GOP 0.619979402677652\n",
      "264 0.7677390835351987 1883 pop,GOP 0.4381306425916091\n",
      "265 0.6987359360251033 492 pop,GOP 0.5386178861788617\n",
      "382 0.651942268253948 31 pop,GOP 0.6129032258064516\n",
      "383 0.6572127443236883 180 pop,GOP 0.4166666666666667\n",
      "716 0.7036927028194111 777 pop,GOP 0.49935649935649934\n",
      "738 0.7244663098214553 909 pop,GOP 0.7282728272827282\n",
      "762 0.7999281440200013 3252 pop,GOP 0.2629151291512915\n",
      "936 0.7758519514783924 2640 pop,GOP 0.35113636363636364\n",
      "937 0.7507973637931492 3250 pop,GOP 0.2969230769230769\n",
      "952 0.7872756124815699 1522 pop,GOP 0.41984231274638634\n",
      "1012 0.7815501174936613 1590 pop,GOP 0.7037735849056603\n",
      "1013 0.7939526622695566 1134 pop,GOP 0.6164021164021164\n",
      "1015 0.7865454323391486 1638 pop,GOP 0.7496947496947497\n",
      "1033 0.6577338451885237 101 pop,GOP 0.32673267326732675\n",
      "1034 0.7363172520640036 84 pop,GOP 0.39285714285714285\n",
      "1557 0.620380004565434 320 pop,GOP 0.525\n",
      "1558 0.6101330171145519 190 pop,GOP 0.7105263157894737\n",
      "1583 0.687227429815954 570 pop,GOP 0.031578947368421054\n",
      "1584 0.7967482535796551 2386 pop,GOP 0.8063704945515507\n",
      "1585 0.7393293619147349 1729 pop,GOP 0.7906304222093696\n",
      "1586 0.7561199595888892 1082 pop,GOP 0.7975970425138632\n",
      "1587 0.7741290794877065 853 pop,GOP 0.8264947245017585\n",
      "1588 0.7228226114669344 1562 pop,GOP 0.8463508322663252\n",
      "1589 0.6984480745579946 1712 pop,GOP 0.8037383177570093\n",
      "1590 0.6619502070464827 68 pop,GOP 0.8235294117647058\n",
      "1591 0.6832782673759562 689 pop,GOP 0.37010159651669083\n",
      "1592 0.6764507960112386 633 pop,GOP 0.33965244865718797\n",
      "1593 0.6057728268694816 216 pop,GOP 0.1574074074074074\n",
      "1594 0.6194644546523835 461 pop,GOP 0.2039045553145336\n",
      "1595 0.6074440864567473 1312 pop,GOP 0.20960365853658536\n",
      "1596 0.6105098390040478 251 pop,GOP 0.33067729083665337\n",
      "1597 0.6151705513883576 405 pop,GOP 0.21975308641975308\n",
      "1598 0.5978376091784948 761 pop,GOP 0.09329829172141918\n",
      "1599 0.6361395634249097 425 pop,GOP 0.04\n",
      "1600 0.6116964172382088 273 pop,GOP 0.05128205128205128\n",
      "1601 0.6289831492726078 218 pop,GOP 0.045871559633027525\n",
      "1602 0.6047832103610471 283 pop,GOP 0.04240282685512368\n",
      "1603 0.6491157966536723 411 pop,GOP 0.0364963503649635\n",
      "1604 0.7536036118416236 2219 pop,GOP 0.7534925642181163\n",
      "1658 0.7798396646613109 209 pop,GOP 0.9186602870813397\n",
      "1699 0.7073809208603119 188 pop,GOP 0.8191489361702128\n",
      "1700 0.7145802286924294 2087 pop,GOP 0.8303785337805463\n",
      "1701 0.703258020434517 108 pop,GOP 0.7592592592592593\n",
      "1702 0.6447798312884762 125 pop,GOP 0.816\n",
      "1703 0.594477112529796 56 pop,GOP 0.8571428571428571\n",
      "1705 0.7559324497758377 24 pop,GOP 0.7916666666666666\n",
      "1843 0.7889815946923925 1031 pop,GOP 0.5354025218234724\n",
      "1844 0.7725113308598417 2240 pop,GOP 0.40714285714285714\n",
      "1845 0.7476060983092937 913 pop,GOP 0.3099671412924425\n",
      "1869 0.4832768348322847 2101 pop,GOP 0.4678724416944312\n",
      "1885 0.6525265921298784 221 pop,GOP 0.5384615384615384\n",
      "1886 0.6500241403486379 186 pop,GOP 0.46774193548387094\n",
      "1887 0.6517246169172226 235 pop,GOP 0.48936170212765956\n",
      "2237 0.6324643493457887 385 pop,GOP 0.7532467532467533\n",
      "2270 0.6312081692716168 135 pop,GOP 0.6666666666666666\n",
      "2271 0.6203985897833778 380 pop,GOP 0.5842105263157895\n",
      "2272 0.6189697954926706 191 pop,GOP 0.6073298429319371\n",
      "2273 0.6318537367337329 265 pop,GOP 0.5811320754716981\n",
      "2274 0.7339882074466174 1499 pop,GOP 0.3709139426284189\n",
      "2275 0.674497195077909 259 pop,GOP 0.20463320463320464\n",
      "2276 0.6979230258464778 1608 pop,GOP 0.7810945273631841\n",
      "2277 0.6700278226657382 1079 pop,GOP 0.6209453197405005\n",
      "2278 0.673412490765833 452 pop,GOP 0.4247787610619469\n",
      "2279 0.6346698044451912 780 pop,GOP 0.33589743589743587\n",
      "2280 0.631987613546603 458 pop,GOP 0.1812227074235808\n",
      "2281 0.7712377698700904 406 pop,GOP 0.6798029556650246\n",
      "2282 0.6695993855077107 2148 pop,GOP 0.7942271880819367\n",
      "2289 0.7853914910389924 1065 pop,GOP 0.05258215962441314\n",
      "2306 0.7179534494340964 2800 pop,GOP 0.8382142857142857\n",
      "2307 0.7044426990846954 1403 pop,GOP 0.3799002138275125\n",
      "2308 0.733667547720905 400 pop,GOP 0.6325\n",
      "2327 0.7480629639728942 729 pop,GOP 0.8902606310013718\n",
      "2328 0.7268459680174212 878 pop,GOP 0.9145785876993167\n",
      "2410 0.675794930912653 26 pop,GOP 1.0\n",
      "2411 0.6471490724154499 17 pop,GOP 0.9411764705882353\n",
      "2412 0.6251542210373221 12 pop,GOP 0.8333333333333334\n",
      "2559 0.5601145773806308 679 pop,GOP 0.36671575846833576\n",
      "2560 0.5501780796836198 202 pop,GOP 0.8415841584158416\n",
      "2561 0.5997463521625094 1018 pop,GOP 0.8948919449901768\n",
      "2562 0.5868076977698692 743 pop,GOP 0.7711978465679677\n",
      "2572 0.69877365199786 1228 pop,GOP 0.6905537459283387\n",
      "2574 0.6272974100749501 232 pop,GOP 0.35344827586206895\n",
      "2627 0.6556797953393565 9 pop,GOP 0.8888888888888888\n",
      "2644 0.7263152226710687 581 pop,GOP 0.8278829604130808\n",
      "2645 0.709493670154282 553 pop,GOP 0.8679927667269439\n",
      "2646 0.6477485108657512 190 pop,GOP 0.868421052631579\n",
      "2851 0.791994270842189 267 pop,GOP 0.9063670411985019\n",
      "2852 0.7691709768961693 395 pop,GOP 0.9215189873417722\n",
      "2853 0.7554327847150164 583 pop,GOP 0.9090909090909091\n",
      "2854 0.7879235416764194 215 pop,GOP 0.9023255813953488\n",
      "2857 0.7909739739278229 407 pop,GOP 0.3046683046683047\n",
      "2863 0.785229716358642 1107 pop,GOP 0.5411020776874436\n",
      "2864 0.7928529177794489 1204 pop,GOP 0.4393687707641196\n",
      "2929 0.7815061640726912 873 pop,GOP 0.40778923253150057\n",
      "2940 0.7862927412072943 813 pop,GOP 0.4194341943419434\n",
      "2942 0.7901823906711405 1033 pop,GOP 0.37947725072604066\n",
      "2947 0.7916765628065784 831 pop,GOP 0.3742478941034898\n",
      "2965 0.5954766606167219 341 pop,GOP 0.7155425219941349\n",
      "2966 0.6335480933453206 1090 pop,GOP 0.6623853211009174\n",
      "2967 0.6045839523219787 915 pop,GOP 0.4907103825136612\n",
      "2968 0.5747052248066605 707 pop,GOP 0.17963224893917965\n",
      "2969 0.5624665505195079 226 pop,GOP 0.030973451327433628\n",
      "2970 0.6136956545813589 1165 pop,GOP 0.8257510729613734\n",
      "2971 0.6462039194165217 1109 pop,GOP 0.7357980162308386\n",
      "2972 0.6917367918250833 1497 pop,GOP 0.8450233800935204\n",
      "2973 0.6899081280570921 1328 pop,GOP 0.8546686746987951\n",
      "2974 0.7168601955622547 1159 pop,GOP 0.8067299396031061\n",
      "2975 0.7610171768573515 1651 pop,GOP 0.884312537855845\n",
      "2976 0.6232073921375708 796 pop,GOP 0.6331658291457286\n",
      "2977 0.6386800450227166 546 pop,GOP 0.8168498168498168\n",
      "2978 0.6458336865318479 1545 pop,GOP 0.8724919093851132\n",
      "2979 0.7110941299755841 1894 pop,GOP 0.9054910242872228\n",
      "2980 0.6867226322460033 673 pop,GOP 0.8826151560178306\n",
      "2981 0.7699793934947206 1756 pop,GOP 0.908883826879271\n",
      "2982 0.7374747527384542 1339 pop,GOP 0.9178491411501121\n",
      "2983 0.763734154934238 1294 pop,GOP 0.883307573415765\n",
      "2984 0.7932747156960396 1434 pop,GOP 0.896094839609484\n",
      "2986 0.7883166262783552 1044 pop,GOP 0.9013409961685823\n",
      "2987 0.7906971667621305 1030 pop,GOP 0.8679611650485437\n",
      "2989 0.7412010492306637 762 pop,GOP 0.8884514435695539\n",
      "2990 0.7506951808916373 947 pop,GOP 0.9060190073917634\n",
      "2991 0.7520365621804322 919 pop,GOP 0.8846572361262242\n",
      "2992 0.692334257537998 2288 pop,GOP 0.8868006993006993\n",
      "3046 0.7860427567470191 159 pop,GOP 0.8805031446540881\n",
      "3068 0.5798122499337788 15 pop,GOP 0.8\n",
      "3069 0.5835629053553852 408 pop,GOP 0.3112745098039216\n",
      "3070 0.5766283165175005 301 pop,GOP 0.38870431893687707\n",
      "3101 0.608494583175568 539 pop,GOP 0.62152133580705\n",
      "3102 0.6096183995413534 547 pop,GOP 0.6727605118829981\n",
      "3103 0.6378865660247479 42 pop,GOP 0.8333333333333334\n",
      "3104 0.6219444430360268 266 pop,GOP 0.6729323308270677\n",
      "3105 0.6215708368965576 62 pop,GOP 0.5806451612903226\n",
      "3106 0.6061941055734148 478 pop,GOP 0.6589958158995816\n",
      "3107 0.605462824266654 185 pop,GOP 0.5783783783783784\n",
      "3108 0.6451915633853026 232 pop,GOP 0.6508620689655172\n",
      "3109 0.6117600569959737 388 pop,GOP 0.4690721649484536\n",
      "3110 0.6057421911319719 434 pop,GOP 0.5806451612903226\n",
      "3111 0.617489964153986 476 pop,GOP 0.6176470588235294\n",
      "3225 0.7931954626497534 3890 pop,GOP 0.8082262210796916\n",
      "3297 0.7322603898744358 2328 pop,GOP 0.6176975945017182\n",
      "3298 0.7255448691301373 706 pop,GOP 0.5991501416430595\n",
      "3325 0.6038623370305513 122 pop,GOP 0.48360655737704916\n",
      "3335 0.7606228275473061 247 pop,GOP 0.29554655870445345\n",
      "3336 0.7570067014617805 551 pop,GOP 0.32849364791288566\n",
      "3337 0.7927570297517583 849 pop,GOP 0.3286219081272085\n",
      "3338 0.7354521118356235 232 pop,GOP 0.2974137931034483\n",
      "3339 0.7812262889604922 1061 pop,GOP 0.32610744580584355\n",
      "3340 0.7297780531866099 709 pop,GOP 0.3229901269393512\n",
      "3341 0.7071277278215875 438 pop,GOP 0.2990867579908676\n",
      "3342 0.7393048018218272 922 pop,GOP 0.3275488069414317\n",
      "3349 0.7330926923861333 264 pop,GOP 0.3712121212121212\n",
      "3350 0.7422782046656548 534 pop,GOP 0.4288389513108614\n",
      "3351 0.7237836054975189 250 pop,GOP 0.364\n",
      "3352 0.7977663252209037 1371 pop,GOP 0.4033552151714077\n",
      "3353 0.69166289358068 797 pop,GOP 0.2961104140526976\n",
      "3354 0.7096662843831829 417 pop,GOP 0.3213429256594724\n",
      "3356 0.6872882418192672 842 pop,GOP 0.33729216152019004\n",
      "3357 0.7545810727848383 1917 pop,GOP 0.35524256651017216\n",
      "3358 0.7724184786209654 636 pop,GOP 0.33962264150943394\n",
      "3359 0.7862648266757768 308 pop,GOP 0.37662337662337664\n",
      "3362 0.7209396485192838 437 pop,GOP 0.2951945080091533\n",
      "3363 0.7820445715288173 908 pop,GOP 0.30066079295154186\n",
      "3388 0.730023016902947 1360 pop,GOP 0.46397058823529413\n",
      "3389 0.3956307675180785 1230 pop,GOP 0.42032520325203254\n",
      "3397 0.7685771670783605 337 pop,GOP 0.8011869436201781\n",
      "3414 0.6980222174129768 1883 pop,GOP 0.7381837493361657\n",
      "3415 0.7318951345416148 2743 pop,GOP 0.7900109369303682\n",
      "3416 0.7974348946346634 151 pop,GOP 0.8874172185430463\n",
      "3599 0.7650633216303876 798 pop,GOP 0.36967418546365916\n",
      "3600 0.7330550465551953 193 pop,GOP 0.31088082901554404\n",
      "3601 0.750522829571578 453 pop,GOP 0.32229580573951433\n",
      "3602 0.704274081703798 368 pop,GOP 0.30434782608695654\n",
      "3653 0.7728290031613132 4121 pop,GOP 0.7990778937151177\n",
      "3654 0.7112893559897768 3682 pop,GOP 0.8039109179793591\n",
      "3813 0.7980644494253872 1796 pop,GOP 0.361358574610245\n",
      "3826 0.7162971625166606 648 pop,GOP 0.7993827160493827\n",
      "3841 0.7655157427280302 1278 pop,GOP 0.607981220657277\n",
      "3842 0.7951924156384449 1419 pop,GOP 0.6448202959830867\n",
      "3997 0.6290746219345437 4342 pop,GOP 0.8012436665131276\n",
      "3999 0.6165713493174145 3763 pop,GOP 0.7919213393568961\n",
      "4000 0.5809330406280977 3713 pop,GOP 0.8063560463237275\n",
      "4001 0.6132240754506477 3948 pop,GOP 0.7857142857142857\n",
      "4002 0.5672139423143457 3620 pop,GOP 0.8038674033149171\n",
      "4003 0.6613005335540604 3614 pop,GOP 0.8024349750968456\n",
      "4004 0.7110194842038811 4217 pop,GOP 0.8112402181645719\n",
      "4005 0.7246659233946878 3723 pop,GOP 0.8076819769003492\n",
      "4006 0.6965572273529866 90 pop,GOP 0.7222222222222222\n",
      "4030 0.6135147096840221 283 pop,GOP 0.43109540636042404\n",
      "4031 0.6104927602074206 921 pop,GOP 0.2725298588490771\n",
      "4032 0.6204671773142648 24 pop,GOP 0.4166666666666667\n",
      "4060 0.5933113979603875 1174 pop,GOP 0.8875638841567292\n",
      "4061 0.7461626618613098 855 pop,GOP 0.8947368421052632\n",
      "4409 0.7569267156043242 140 pop,GOP 0.18571428571428572\n",
      "4506 0.7966573013633084 1409 pop,GOP 0.8105039034776437\n",
      "4524 0.7919429932852394 958 pop,GOP 0.4331941544885177\n",
      "4720 0.6832204780233088 38 pop,GOP 0.8421052631578947\n",
      "5383 0.6675378226715296 230 pop,GOP 0.8826086956521739\n",
      "5384 0.6331562211987063 910 pop,GOP 0.8835164835164835\n",
      "5393 0.7240674312549582 277 pop,GOP 0.35018050541516244\n",
      "5396 0.7892533918002412 1008 pop,GOP 0.46924603174603174\n",
      "5400 0.6879742639662427 576 pop,GOP 0.3298611111111111\n",
      "5403 0.6785297171636022 685 pop,GOP 0.34890510948905107\n",
      "5404 0.6873900431254639 744 pop,GOP 0.2916666666666667\n",
      "5405 0.6892693159834274 435 pop,GOP 0.2827586206896552\n",
      "5429 0.703972997014433 519 pop,GOP 0.3333333333333333\n",
      "5430 0.69723258994431 1403 pop,GOP 0.6329294369208838\n",
      "5888 0.7603839956635171 1527 pop,GOP 0.6051080550098232\n",
      "5889 0.7771830604247671 1580 pop,GOP 0.6867088607594937\n",
      "5890 0.7599454032980361 2433 pop,GOP 0.6695437731196054\n",
      "5891 0.7393414128142715 1535 pop,GOP 0.6052117263843648\n",
      "5892 0.7597911613302526 1671 pop,GOP 0.6959904248952723\n",
      "5893 0.7900751432676568 1379 pop,GOP 0.6156635242929659\n",
      "5894 0.7815501188610011 1692 pop,GOP 0.6991725768321513\n",
      "5895 0.7645274481530743 1899 pop,GOP 0.7509215376513955\n",
      "5896 0.776347559973462 4620 pop,GOP 0.8073593073593074\n",
      "5899 0.7619288916226351 857 pop,GOP 0.6021003500583431\n",
      "5900 0.765271837989617 1531 pop,GOP 0.549967341606793\n",
      "5903 0.7523805258790073 1294 pop,GOP 0.731839258114374\n",
      "5905 0.7793326124069577 1686 pop,GOP 0.7485172004744959\n",
      "5906 0.7609819876707263 1920 pop,GOP 0.6901041666666666\n",
      "5907 0.7793302144661808 2217 pop,GOP 0.7830401443391971\n",
      "6031 0.6659995280575908 1044 pop,GOP 0.5153256704980843\n",
      "6032 0.7863561173615321 299 pop,GOP 0.4782608695652174\n",
      "6146 0.6560755609116868 314 pop,GOP 0.643312101910828\n",
      "6215 0.6750365826857948 1257 pop,GOP 0.8337311058074781\n",
      "6261 0.6307021969425421 679 pop,GOP 0.6730486008836525\n",
      "6262 0.6141088211669434 160 pop,GOP 0.8875\n",
      "6263 0.6058409813993118 76 pop,GOP 0.8421052631578947\n",
      "6264 0.6282814744299984 571 pop,GOP 0.8126094570928196\n",
      "6266 0.6363989110006285 326 pop,GOP 0.6226993865030674\n",
      "6267 0.6106044747754906 461 pop,GOP 0.9197396963123644\n",
      "6323 0.7258559006904928 939 pop,GOP 0.7880724174653887\n",
      "6324 0.7910653957635067 189 pop,GOP 0.7883597883597884\n",
      "6527 0.7985899812247109 1204 pop,GOP 0.25332225913621265\n",
      "6769 0.7729305796852665 1794 pop,GOP 0.5953177257525084\n",
      "6770 0.777159217670682 2112 pop,GOP 0.6444128787878788\n",
      "7209 0.7615863101752322 5089 pop,GOP 0.6225191589703282\n",
      "7245 0.7867466265176566 3336 pop,GOP 0.5356714628297362\n",
      "7246 0.7310441594763861 5258 pop,GOP 0.5283377710155953\n",
      "7352 0.7471295354902484 276 pop,GOP 0.3007246376811594\n",
      "7538 0.7754807914694216 8112 pop,GOP 0.6049063116370809\n",
      "7541 0.7936112397770996 4960 pop,GOP 0.5173387096774194\n",
      "7543 0.7979015553742309 1008 pop,GOP 0.3978174603174603\n",
      "7545 0.7864556582906477 3022 pop,GOP 0.43348775645268034\n",
      "7556 0.7608642077474354 1923 pop,GOP 0.6994279771190848\n",
      "7557 0.71955541735974 2221 pop,GOP 0.5141828005402972\n",
      "7787 0.786951797071404 3133 pop,GOP 0.2170443664219598\n",
      "8060 0.5447960796058752 1682 pop,GOP 0.28240190249702735\n",
      "8168 0.7957120936271149 1167 pop,GOP 0.40445586975149955\n",
      "8171 0.772849715462963 1968 pop,GOP 0.3714430894308943\n",
      "8173 0.7888117217589639 2431 pop,GOP 0.42245989304812837\n",
      "8174 0.7980573564112953 1621 pop,GOP 0.4108574953732264\n",
      "8177 0.7664965177321161 811 pop,GOP 0.3884093711467324\n",
      "8182 0.7516380351241537 2789 pop,GOP 0.3477949085693797\n",
      "8185 0.7857355944513014 301 pop,GOP 0.4186046511627907\n",
      "8186 0.7693954985138732 1277 pop,GOP 0.3155833985904464\n",
      "8187 0.7770630582829996 535 pop,GOP 0.36261682242990656\n",
      "8194 0.7997583641593425 107 pop,GOP 0.5233644859813084\n",
      "8214 0.7970171444752052 1647 pop,GOP 0.34365513054037644\n",
      "8215 0.7905482711936779 871 pop,GOP 0.3409873708381171\n",
      "8216 0.7866266977922897 402 pop,GOP 0.39303482587064675\n",
      "8231 0.7985203076608901 3387 pop,GOP 0.35577206967818126\n",
      "8232 0.7944924670699154 2355 pop,GOP 0.3796178343949045\n",
      "8235 0.7882499995278108 1006 pop,GOP 0.39065606361829025\n",
      "8346 0.5105589105427522 1669 pop,GOP 0.29898142600359495\n",
      "8347 0.5147141054156975 363 pop,GOP 0.3140495867768595\n",
      "8538 0.7989498258907041 389 pop,GOP 0.5269922879177378\n",
      "8543 0.4829535684105941 437 pop,GOP 0.36613272311212813\n",
      "8554 0.5078827105038424 1080 pop,GOP 0.41759259259259257\n",
      "8556 0.4850601769969872 284 pop,GOP 0.35563380281690143\n",
      "8558 0.49405530454661906 494 pop,GOP 0.37449392712550605\n",
      "8560 0.5035864619506418 699 pop,GOP 0.4434907010014306\n",
      "8563 0.4877659819099495 49 pop,GOP 0.32653061224489793\n",
      "8565 0.5353952995879409 1434 pop,GOP 0.29567642956764295\n",
      "8566 0.5002831005766849 803 pop,GOP 0.2752179327521793\n",
      "8567 0.5974514409446311 113 pop,GOP 0.7876106194690266\n",
      "8569 0.6082921966932394 142 pop,GOP 0.6830985915492958\n",
      "8570 0.5045783289081662 1781 pop,GOP 0.3116226838854576\n",
      "8571 0.5218219214763411 1457 pop,GOP 0.3843514070006863\n",
      "8572 0.5459999169543791 723 pop,GOP 0.3831258644536653\n",
      "8573 0.4137425639139753 193 pop,GOP 0.3005181347150259\n",
      "8574 0.4871698783534406 306 pop,GOP 0.3333333333333333\n",
      "8575 0.48684793759562717 1062 pop,GOP 0.3465160075329567\n",
      "8577 0.5379122186933534 2214 pop,GOP 0.3626919602529359\n",
      "8581 0.4946796364480695 713 pop,GOP 0.35624123422159887\n",
      "8582 0.4824166139768844 70 pop,GOP 0.38571428571428573\n",
      "8584 0.542018873679853 1528 pop,GOP 0.42997382198952877\n",
      "8586 0.4905136438538168 183 pop,GOP 0.5081967213114754\n",
      "8588 0.49466448658815837 2329 pop,GOP 0.3714040360669815\n",
      "8590 0.486772544420078 684 pop,GOP 0.32748538011695905\n",
      "8712 0.7654228186931529 920 pop,GOP 0.37717391304347825\n",
      "8792 0.7931320191014035 193 pop,GOP 0.32124352331606215\n",
      "8808 0.7800310117868298 2345 pop,GOP 0.6285714285714286\n",
      "8809 0.7542791095432019 2997 pop,GOP 0.5972639305972639\n",
      "8834 0.7846531531628974 1628 pop,GOP 0.5755528255528255\n",
      "8835 0.7983722592552188 201 pop,GOP 0.4626865671641791\n",
      "8884 0.47106888058508173 777 pop,GOP 0.4555984555984556\n",
      "8885 0.7763674126654557 1362 pop,GOP 0.43392070484581496\n",
      "8893 0.7913017569602452 2322 pop,GOP 0.5180878552971576\n",
      "8896 0.792881621521599 1823 pop,GOP 0.43664289632473946\n",
      "8897 0.7239102902010065 829 pop,GOP 0.4209891435464415\n",
      "8898 0.7638709777853305 755 pop,GOP 0.423841059602649\n",
      "8899 0.7905542771062389 829 pop,GOP 0.4077201447527141\n",
      "8900 0.6162499690724428 1320 pop,GOP 0.5037878787878788\n",
      "8902 0.7982433579985567 2576 pop,GOP 0.359472049689441\n",
      "8944 0.717222026256419 1843 pop,GOP 0.4639175257731959\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD4CAYAAAD4k815AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAACO2klEQVR4nOydZ3gUVReA39ma3kN6J0CoAUILEEJvUgRRunQEsVFERQUrTVAERYqACJ8UxULvvbeEnkASAoQAIZteNlvu9yOwGkEFBQJhXp592Jm5M3vunc3ZO+eeIgkhkJGRkZEpOyhKWwAZGRkZmQeLrNhlZGRkyhiyYpeRkZEpY8iKXUZGRqaMISt2GRkZmTKG6lF+mJubmwgMDHyUHykjIyPzxHP06NGbQgj3e23/SBV7YGAgR44ceZQfKSMjI/PEI0lS8v20l00xMjIyMmUMWbHLyMjIlDFkxS4jIyNTxpAV+1PKokWLiIyMpGHDhhw7duyOY0FBQURHRxMdHU1KSgoA+fn5DBo0iObNmxMdHU1GRgY6nc7SLjo6GrVaTUZGxj9+xvTp0y3nBAUFMWrUKAD2799PZGQkUVFRTJ061dK+RYsWREdHExERwQ8//PAwh0ZG5slHCPHIXrVr1xYypY9OpxM1a9YUer1eJCYmioYNG5Y4PnDgQOHn5yciIyPF0aNHhRBCLFy4UHh7e4vKlSuLV155RTRr1kw0adJEbN26VUybNk24u7sLW1tbIUmSaN26tdDpdMLX11e4u7sLjUYjHBwcxJYtW4QQQvTv3194enqKgQMHCiGEaNu2rdi/f78QQoiIiAiRnJwshBCiXbt2Ii4uTgghhF6vF0IIkZWVJQIDAx/+IMnIPEYAR8R96Fp5xv4UcvDgQRo3boxGoyEoKIhLly5Rv359GjZsyI4dO9i8eTNarRa9Xk+LFi3w9/fnlVdeIT09ncuXLzNr1iw0Gg15eXmMHz+eo0eP0r9/f5ycnFAoFGzcuJHw8HCaNGmC0WhEkiSMRiOtW7fml19+4aOPPsLb25u1a9cSHh5ObGws9evXByArKwt/f38AIiIi2LFjBwAajQaAvLw8qlSpUirjJiPzpCAr9qcQnU5HcnIykZGR1KtXj5s3b7J8+XI6d+5Mu3bt0Ov1tG7dGgcHB7KysrC3t6dnz57o9Xry8vIQQrBhwwbUajWdO3dm+fLlTJ8+nbS0NJydndFoNKSnp7N161bUajV6vZ7q1aujUqkICQnBx8eHSZMm0b59e3r27El+fr5FNjc3N2JjYykqKmLLli3odDoATCYTTZo0oVq1anTq1Km0hk5G5ongkfqxy5QOScv3cG7Jfst2TOxmtl/ZyeiXl3Dy/D6O6I+yYPIevv1pDlqVHS7Wfnz/7Q98XP9LYrSxXIi7QNL5JCQkqruW58TNC6iUKvKu5rFnxR7qlq9LTPIpVDYuZGRcRaVQ4eVWEWdHDxKTj6NVaUk4k4DJaGLvV3vJ9M/kbNJZrsVeY+e6nQQ6B7J74m4AXqr7EkO6DUEgcLZ1JudoDrsn7kaSJD5p+wlZUVkMHjsYvxQ/7Kzs/rrT0r2Pj1KjJPzFcKxdrP/tEMvIPFZI4hHmY4+IiBBygNKj51v/t7ly2cqyfYpTrGc9IxnJz/zMKU7hhhueeHKDGxTd+leOciSTjC225FM8q1agwIgRJUrMmAGoQAUSScSMGRMmnHHGDz/SSMMWWxJIQIECN9zoQQ+ccCKRRH7kRwoppD3tqU3tEjIbMbKc5XSmM1ZYISGhQIEBA3OYw0u8hOoBzkvaf9OeiKERD+x6MjIPEkmSjgoh7vkLKs/YnwKMRjMuDtn0jR+FnZM7X0eHc/OkxHr7T4jXKXGwdqBt1878+tNKvH18ORcfB4DwFqhSVfR8sTeLFi7ARuuAqVBHjhlMmJg6ZSrJyclczcgk7n/F55QrV45KFStRvnx5MgIr8PPH76OR1Li6uBIYGIhnR09GjRzFzp07iX0zlm7PdePbb79l4cGFODo68vmMz1m7di0A00ZOo22btqRcSaH3i71RKpXo9Xo+H/Y53V/o/pf9vZ/JSv7NfL7w/wKzwfwfRlhG5vFCnrE/BUzzfAVtFjQN3IJCwOQb11mXnY2NK9RwlVgfZ0Tr5E5Bdgbubm6kZ2VjKCxAAdjZ2JJXkI/JXKz4JABJQgiBJElYWVlhMpkoKipC4eyCOUMHCiUqB0dUAYEUnj2NZCjCysqKRYsW8fzzz/P222+zadMmrl27RsWKFbl69SqxsbFotdpHPjb56flMdZtKmxltqPdqvUf++TIy98L9ztjlxdOngOseWlKdIfnZOtzExP78fNyUKvTRbmy/YkIhgUJjhdlkwtnZmQoNWyLZ2mGttUcSVthoHbHR2qGQFCgpnhEHBwcTFhaG2WxGCIHa2obwrsWzaK29PVqNGunKJYbMnE2jRo04ePAgzz//PAAjRozA1taWChUqUFhYyPjx40tFqQNIUrExfsf4HWwavYnCzMJSkUNG5kEiK/anAKVSTZGdhuYfzOfUqDE4ValCtTatMGxRkJsLdUN9CWozCK2dPReSLnLu8gVUFSpTUJiD1pzP8vCGTAkJB2HGdOua3t7ezJs3j65du2IwGDAU5JOwcil2dna42FjToHo1nDRqvhrQh127dlGtWjWLPD4+PuzatYvt27ezb98+evToUSrjAqCyUiEpJQozC9k/bT/z6szjxukbpSaPjMyDQFbsTwHaDCMOOolZvbuwf/FmXKWqRHm9ztiOs1FKKpSqugh7H0wBwVAhDJePphMaWpHGjg70dnOhgrNga0YC1T08CPL1pXv37kydOpXIyEji4+Np0qQJAwcOJDMzkyVLltCuXTs+++wznJ2dUake72UctY2a4aeG8/LZl+m/uz9FuUXMi5hHzHcxpS2ajMy/RlbsTwHWUrFHjI99Feq4BONNPp2c1LR1d8Zea8OpxO1o9s1j57dzCCzM5fXzR7BLOsdXe/eS1rQpg/Lz2JqXx1erViFptUyaNIn333+fihUrkp6ezrvvvgvAu+++y4cffsj69esZNWoU1tbWlvQCjzNuldxwq+SGfyN/Bh8ZjEd1D1YPXs3J/5382/NOLDnBNO9pHJ179BFJKiNzb8iK/SlAKSnQ2NgQ5l6PpkFRHEo5icFk5FpROoFOPng5lGPdB4vx8fSknLs7zz33HJIkUaVKFRYvXsycOXMICwsjMjISgICAAF5++WWCgoI4d+6cZVb+3nvvsXfvXlJSUvjuu+/Izs7GycmpFHt+/zj4OND9t+4Ik2BVr1VsfnMzN8/dJGl7EpOcJjHFdQpzI+ay/Nnl/Nz3Z3JTc1kzdA2feXzG4a8Pl7b4MjKA7O741CABSicVTsKe7l070mvvOCRJ4svPZ7Bq5lJqD2yGX0gAM2bM4LPPPsPT09Ny7pIlS6hcuTItWrTg6tWrtGjRgn379lG5cmVCQ0PR6XTY2tpy7tw5cnNzsbe3x2QyMWfOHMvi5JOEnYcdb2W9xcZRG9k3dR/7pu6zHKv0bCWSdyaTejQVgMrPVcYpyIl9U/ex7uV1FOUW0fDNhqUluowMILs7PhVMDR5PUb6eai/aUkuKBkDhb4XGzhZhNKOPz8C1TxjWVdxKVc7Hkcv7L6O7oMOQZ8AtzI3AJoEIs2DDGxswFZlo9FYjnAKciF8bzw/PFGedHJs5FitHq3+4sozMvSMHKMncgZWdHcbCIm7kXeJI4UZ8bELxzCuPqUiJMJlRe9mi8bMvbTEfS/wa+OHXwK/EPkkh0XZG2xL7KrSvQHj/cGIWxnBxx0Uqdar0KMWUkSmBrNifAhzdPbBzdKP/rImlLUqZpvbQ2sQsjOGnHj9R95W6eFTzQGOv4cqBK3hHeBPWJeyJNE3JPHnIiv0pQAhxX0mxZP4dXjW9sHGzIf9mPvum7LvjeGj7UCJeisCzpicOPg5A8b1JPZaKxk6Da6grkuL3GyXMAmOhEbWN+pH1QaZsICt2GZkHhFKj5NXEV7l+4jqJmxNxCXWhKKcIR39H4n6L4+ico5xfex4Av0g/HAMcyUzK5MqBKwDYlrOl59qeeEd4o8/WM8lxEgA+9XwIiApAbaPGL9KPjW9sJPdaLsNODsPeWzahydzJPyp2SZKsgF2A9lb7H4UQ4/9wfDQwFXAXQtx8WILK/Ace3fr4U4/WXot/Q3/8G/qX2B/aLpQ6w+tw6KtDqLQqzv50lsv7L+Pg40CLyS3ITsnm0JeHmFdnHsNODWNZp2UAOPo7UpRbxIEvDtyRqCx5dzJVX6j6yPom8+RwLzN2PdBMCJErSZIa2CNJ0nohxAFJkvyAlsClhyqlzH/CZDCh0soPZ6WNR3UPOszpAECLyS0QJoHGTmM5HtIyhJXdVjK76mwAbNxseOXCKyjVSgB0CTquHb/Glre2kJGQgU9dn0ffCZkngn/8a79Vby/31qb61uv2HPBz4E3g14cincwDwWw0o7CVY9EeJ9TWd9rNKzxTgUGHBhG/Jp7yrcvjVcurxHGXEBdcQlw4veI0GQkZfBn8Ja2mt8JYYKTKC1VwCXEp0V6fref8+vPFZh8/x4faH5nHi3uaxkmSpASOAuWBr4QQByVJ6gikCCFi/26lX5KkIcAQwFLLUubRYjaYUahlxf4k4FHNA49qHn/bpvN3nTmz8gwAm0ZuAuDSnkv0WtfL0kaXoOO76O/IvpINQOSbkbSY1OK+vHLMRjP7PttH/s18HHwdqDuiLgrVo/0eCbMgIykD52Dnv5T95P9Okrg5kcrdKhPcMtjyhPM0c0+KXQhhAsIlSXICfpYkqTowDmh1D+fOBeZCcYDSvxdV5t9iNprlL3sZQm2tZrwYj6HAwJ6Je9j10S7Mxt/t73NqzuFazDUAmn3arDh6dso+VFoVOak5eNf2pvaQ2hYPHCEEsd/FkpGUQZ3hdbDzKC45mLA5ga1vb7Vc18bdhuq9qlu2k7YncXbVWWoPro17FXcUygej9HOv5SKEIHFLIutHrEefrccl1IWea3riWsG1RNsdH+xg54SdAMQsirHsf9rz69+X4VUIkSlJ0g6gExAE3J6t+wLHJEmqK4S49sCllPlPmAymRz7Tknn4qK3V6LP1ACRuTuT0itMkbE6wKPUhR4fgVcsLnzo+fN/ye3Z9tAuA4/OPY+VshVslN+aEzylxzcTNifTf1Z/ELYnozhcXEq/zch1OLDnBtnHbuLDuAn4N/Yh4KYKtb20l5VAKh2cdpmKninT/5a+rWv0RQ74Bs8nM6kGr0TpqiV8dT6UulYh4KYIzP55h14e7SrR3CnRCd17HrIqzAAhsGohHDQ8ubr/I9djrAPTZ3If4tfEc/OIgABte24AQgsDoQDxrePK08Y8pBSRJcgcMt5S6NbAJmCyEWPOHNheBiH/yipFTCpQOM0Nn4l3Hm67/61raosg8YPJu5LFl7BYSNieQk5Jj2f/y2Zdxq/R7ioj08+kIsyA3NZfvmn5HcItgErckWo5bOVlhMpgw5BuInhDNjvE7AHAp78JLsS+xeexmDs/6PcmZY4AjWclZlm0bNxuaTGhC7SG1//LpcMtbW9g7ee9dj0lKCWH6XRd5VPegwegGVH2hKkIIVg9ezYnvT1iOq23V2HnYUZhZiHtld/rt6ockSVzef5kFkQtKXDu0fSjdf+3+wJ4oSoP7TSlwL4q9OvAdoKQ4G+QKIcSHf2pzEVmxP7bMCJpBQFQAnb/rXNqiyDwkjHoj109cR6VV4VH9r230eWl5LGm9hGvHr6F11FK9d3XafNGGizsu8n3L7/Go7oFXLS+LWeO1pNdwCnRCmAXxa+LxqOHBxtc3ci3mGsGtgqk1qBbrX1lPysEUACp3q4xrBVeq966OWyU30s+nk5GYgW99XyY7TQaK/fWdQ5wx6U1EDIugcrfK5F7L5cAXB7Bxs8G9sjuVu1ZGqbn7D8RtnfV36wXZV7I5u+osG17bAECzT5oR1DwI79reT+TT6wNX7A8SWbGXDtN9pxPSOoRO33YqbVFkHhOK8orQ2P7uamksNDLJaRJqGzUhLUM4veI07+S9c89Rr4Z8A4uiF3H18FUA7LzsGLh/IN81/Y7MpEyUWiUmvYmAqAD67ez3MLp0V3Z9vIvt722/Y//99O3PLFq0iLlz5yJJEjNnzqRWrVqWY9OnT+e3334DIDk5mS5dujBt2jT279/PqFGjUKlUdOjQgTFjxqDT6ejSpYvl3L1793Ljxg2cnZ3v+ExZscvcwWcen1Hp2Uo8880zpS2KzGPM8YXH+W3gbyDAs6YnQ44OuW8vGn2OnqzkLL5r+p2lfmy1ntWwcrHCztOOeq/UQ+vwaOvbXthwgaVtl971WFjXMBqManBHore/IiMjg+bNm3PgwAEqVapEUlISAMOGDePrr7+2tPP09OT69euWbUmSuHjxIkOGDGHjxo0AqNVqdDodSUlJ1K9fn/z8fEt7nU5XQsHLil3mDqa4TqFqz6q0m9mutEWRecy5ee4mN07fIKRlyH9SwHG/xbG8y3L8G/rTY3WPR67M/4qY72L4td/dw246zOuA1kFL5W6V//IHbcOGDaxfv56uXbvSpEkTqlSpwoABAxg1ahR/1KVpaWlERUVx9uxZFAoFdnZ2ZGdnI0kS7dq1o1OnTgwfPpx27drx/fffU7VqVSpUqEBGRgZxcXHk5eWV+Fw5ba/MHcheMTL3yu0ygf+Vih0r8k7eO49dxHP4i+HU6FuDazHX+LX/rxavGoDVg1cD8Hr913H0d0QIQVFOEVlXsjmx8SKVWgZy8+ZN7G3s+fDDD7GyssLV1ZUePXowatQorly5gq+vLwA//PADzz//PGvWrEEIQdWqVYmNjQXg9OnTNGxYXIxFCMHNmzdJS0sDip8ItNr//iP4eI26zEPBbJD92GUePY+bUr+NJEl41fTipZiXEGbB/un72Txms+X4FwFf3PW8vcB5znOBC6T76dBqtWRlZeHiUhzxu2fPHrp3L3b5XLp0KUuWLCE6OhoHBwfmzZvHqFGj0Gg0JCcnM27cOBQKBQ0bNuSXX36hqKiIb7/9lvbt26PVasnIyLirrf1ekadxTwFmoxx5KiNzNySFROToSMaL8bxb9C6tpv19zKUvvlziEqZUE4WFhdjZ2Vlm2I0aNQIgPj4eSZIIDQ3l6tWrvP3221SpUoUNGzZQVFSEg4MD0dHRKJVKli1bxm+//YaDgwPTpk3D3d2dSpUqcf78+f/UL/mvvYwjhChW7LIpRuY+GTRoEPb29jg4OLB0acnFx06dOuHk5ISTkxNqtZqIiGLz79y5c7G3t8fR0ZH27dsDcOXKFYKDg7G3t8fe3p433njjkfflXlCqlTQY2YDXkl5jwL4BvHH5Dd43v0/Qm81wHdII14GR+A5qQV1VffKkXPR6PRMmTOC9994DsJhhlixZQq9evfjkk08AeOutt5g+fTpNmzYFij1nNm7ciFKpxGw2c+bMGfLy8ti7dy9CCI4dO0Z6evp/6ou8eFrGMRlMfKz5mKYfNSXq3ajSFkfmCSEpKYmKFStSs2ZN8vPzSUxMLLGg90e3vt27d9O8eXM2bdqElZUVoaGhODs7c+zYMX766SeqVKlCeHg4wcHBGI1GTp06RUZGBra2tqXVvfsiPSWXlPhMdi+PByBr+QFS/a+zIX8dFy5cAGDw4MFYW1vzww8/cOPGDQAcHR2xs7MjJSXFcq3KlStz9uxZAFQqFefOnSMjI4N69eohhEClUuHt7c3FixdLyHC/i6cIIR7Zq3bt2kLm0VKUXyQmMEHsnrS7tEWReYJ45513hLW1tdDr9SIxMVFIkiSysrLuaHfmzBkhSZLYv3+/EEIISZJEcnKyEEIIFxcX0aFDB5GSkiLs7OyETqcTe/bsEQqFQuTn5z/S/jwITCaziE/eL97ye09MbDz3kX42cETch66Vn8/LOLeLM8imGJn74cSJE7i4uKDRaAgKCkKSJM6dO3dHuzFjxmBlZUX9+vUt+w4cOEBubi65ubkkJCRw5coVypcvT5UqVWjWrBmenp5cvnz5UXbngaBQSIT618ffyYsgN4fSFudvkf/ayzgmgwlA9oqRuS+srKwwGo2WbSEETk5Od7TbvHkzXbv+noOoQoUKDBkyhICAACRJsphb4uLiaNq0KW+99RbXr1+/64+EzINDVuxlnNuJlf5YJFlG5q/YNPdbZg99ieZNo0lPTyc/P5/9+/cjSRIBAQEl2m7cuBGj0cj7779v2ffTTz9Rv359atasCUCXLl1wdHTE1taWPXv2cPbsWWxtbbG3l2u1PkweT0dTmQeGELJil7l3zh8LAikIj4KDODo6Uq5cOQBCQkL45ZdfWLx4MWvXrgVg3LhxODk5ERoaajn/nXfe4eDBgwgh8Pf3Z+DAgSQnJ6PT6dDr9axbtw7A4iEi83CQFXtZ57bTk6zXZe4BV28jWWn5PPvaaLKdPZg/fz6SJDFjxgxUKhXR0dGWtu3atcPd3b3E+U2aNCE7u7hq05gxY3B3d8fd3Z1evXpx+fJlbGxsmDFjxqPs0lOJ7O5YxslJzWG693Taf9OeiKH37i0lIyNzd2ZXn41LeRdeWPXCI/vM+3V3lG3sZRxh/ufc1TIyMmULWbGXdWRTjIzMg+cxr94sK/YyjmXxVJ6x/2sWLVpEZGQkDRs25NixYyWOTZ8+nejoaKKjowkKCmLUqFEArF+/njp16tC4cWN69epVwnVQ5snmSfhbkhV7GcdiipG9Yv4VGRkZfPnll+zYsYMlS5bw6quvljg+cuRIduzYwY4dOwgLC6Nbt24AvPfee/z444/s3r0btVrN5s2b73Z5GZmHgqzYyzqyKeY/cfDgQRo3bmyJwMzNLU7+9GfS0tIslXAAqlSpQmZmJkIIsrKy7vAekZF5mMjujmUc2RRzf2TlG6jx4SYAqng7ICUcR3nxIpGRkaSlpZGamkqjRo2YM2cOtWrVsiTDOnXqFJmZmfj5+aFSqTAajfzwww8olUqqVavGqFGj8Pf3t4TSX7p0iYsXLxIeHk7v3r0ZOXJkaXZbpowhz9jLOrdm7LIp5t5IySywvD99NZuDydls27mHlz+ZjUajQQjB999/bzHJ3DbFhISE0LBhQ1auXElYWBgGg4GEhAQyMjI4ffo02dnZaLVaduzYwerVqzEYDLRs2ZJDhw7xzTff3FEK7VHyoNYQdDqdpW10dDRqtZqMjIzS6NJTj6zYyzi3beyyKeb++KZ3bVa+1ABJnUm+sYgxS95F7eODJEl3mGTi4+MxGo3cuHGDkJAQkpKScHR0xNnZGaVSicFgIDIy0nJta2trzGYz3bp1o6CgAGtra9Rqdan080GuIbi4uFjaTpkyhebNm/+nKkAy/x7ZFFPGkU0x/47z17LJc7+B8eZyzPpcUhesIRXQarXUqlWLzMxMJk6cyKxZs9DpdJZx9vX1RQiByWQqkQ9l9+7dnD9/nq1bt3Lp0iXMZjNDhgxh+PDhvP7662g0mlLp51+tIfy57uZfrSH4+/vfdQ1hyZIl9O7d+5H1Q6Yk8oy9rCObYu4LXxdrQvTptBjRCZ/nBiAO5cAfPBX1ej0tW7YkMzOTt99+G71eX6I6fVFREUCJ/CkAJ0+epHHjxly8eBGzuTiVshCCN954g9mzZ5coxvAo0el0JWbVjo6O6HS6O9rdLs58m759+9KmTRsqVapUooISgNFoZM2aNXTu3Pmhyi7z18iKvYwjm2LuDwcrNV+d+QGAE4UFKPR3RqLMnDnTkj/lbh4ykiQRFxd3x/4tW7bcsW/WrFkolUpyc3MfgPT3TlFREefPn8fe3p7MzEzL/j8WZ/4jS5cuLTEDHzp0KIcOHSIuLg4XFxdWrlxpObZp0yYaNGiAnZ3dQ+2DzF8jK/YyjmyKuT+EEIjkiwAkS2Z0JtMdbcxmM/b29tSsWZPWrVvfYUd+66237jhHkiSsrKzu2J+fn0+bNm2oWLHig+nAPbJgwQKWLl3Knj172LNnDwaDgUuXLpUoznybPxZnvo1SqbT0293dvcQsXzbDlD6yYi/ryH7s94UkSbgOewkBXDf8YbYulTRn2djYcPz4cXJycizmFyiuYxkbG3vHdYUQd/Vlr1q1KsuWLXugfbgXbivpzp07M3z4cJo0aUKPHj344osviImJYerUqZa2t4sz/5GPP/6YZs2aERUVRWxsrOV4bm4u+/fvp2XLlo+uM6XAo0ye+G+QszuWcW6cvsHsqrN5bsVzVOlWpbTFeew5uW0Tm+Z8CcCuuCR+iz0DgLU1FPzuCUndunU5ePAg8fHxNG7c2FLAODg4mISEhL99QnJ0dCQrKwsodid84403HlJvZB4G39T4BudgZ174Wc7uKFNKyNkd74/87GKFa6W0RW80cnvY/qjUoXhmHhMTw/Dhw7l586Zl/+uvv/6X11Yoiv/cbit1gLlz51pcCGVkHhSyYi/ryKaY+6JKVDMiPNrQyX8E9byaotXe6REcFhbG4cOH6dGjB19//bXFy6VSpUq88sord72uQqHA2traotxvk5GRUWLhUebJ4HE3xciKvYwjl8a7P+xcXPH0qgVAJRdBLU9ViacdZ2dnXFxccHJy4uzZsyxZsgQonsGfPXvW0s7Pz8/y3tbWlgMHDuDr62v5EYBiZZ+QkPCwuyTzoHkC/pRkxV7Wue3tKJti7ombl5NZGzOJpYZ1tA74gVzsLXlh7O3t2bRpE7NmzWLMmDEA9O7dGx8fH6ZPn17iOu+++y6+vr6cOHGC3NxcatWqhY2NDd27d6dJkyY4ODiwd+9ebG1tS6ObMmUcWbGXcWQ/9vvjYuwxBGZUV06yMDmKXv2GsXDhQqysrNi2bRsqlYrNmzdbFPuSJUsYO3bsHSaYJUuWsG7dOqpVqwYUuwdOnjyZy5cvU1BQwKRJkyxRnDJPII+3JUZOKVDWkf3Y74/a7TujsbZm89xZOJbzoP+493hz/Acl2oSHh1vef/jhh3e9zq5du+7Y17JlyzLvBvg08CT8LcmKvawjpxS4LyRJonrzNlRr2gok6Yn4I5Z59MiLpw+Zv0s5epvx48dTvnx5y/b+/fuJjIwkKiqqRCDGgAED8PLyYtCgQQ9d7kfFbVPMqq2r7jk1a5s2bYiMjMTNzY3q1atTr149Fi1aZGlbv359XF1dsba2pnLlysDdx/RJTuMqKRSyUpe5KwqVAmF6vBV7cQj137wAK+AQEAucBj64tX8qcA44AfwMOP3TtWrXri0eJDqdTtSsWVPo9XqRmJgoGjZsKBYuXCgaNGggIiMjxdGjR8W1a9dE9+7dRUhIiFi4cKEIDAwU9vb2ol69euLKlSuiXbt2Ii4uTgghRL169US7du3EwIEDxeXLl0WTJk1Eo0aNRGRkpDh8+PADlf3f8Oe+/fmYv7+/cHR0FLVq1RJbt24VQgjRtW1X4YuvUKvUQqFQiuCg8qJSxTBxcMdu4eTkKBQKhVCplCI6uoHIyY0XTZs2EAEBvqJChWBha2stlEql0Gq1QpIkUbVqVVG7dm3h4OAgIiIihLu7u6hZs6YIDg4WgFCr1UKr1QobGxsREREhwsLChLe3t2jcuLHo16+fcHZ2Fo0aNRLlypUTrq6uYuDAgaUxjDIy/4n59eeL71t9/0g/Ezgi/kG//vF1L4pdAuxuvVcDB4H6QCtAdWv/ZGDyP13rQSv29evXixYtWliUXUhIiKhRo0YJRf/yyy+LoUOHCpVKJRYuXCg++ugj4e3tLSIiIkSjRo1E1apVxddffy0WLlwoXFxchLOzsyhXrpx48803RUREhIiMjBQrV64UjRo1KvHZkyZNEs2aNRNNmjSxKNGvvvpKhIaGipCQkAfaTyHu/iP2x2MhISHizTffvOPY6dXxoilNhbOVm1Ap1CKwXEXhZucgQtxdhK2tQkz9zEt8v8RPlPNQiqrVrIRajXB2VojlK/zFz78ECK1WKRwcHATFRh3Ly9raWgCic+fOonXr1ncc9/f3FzY2NiX2OTs7C51OJ6ZPn27ZJ0mSCA8Pf+DjJSPzsFjQaIH4rtl3j/Qz71ex/6ON/dZFb6eeU996CSHEpj80OwA8d8+PCf+B9ItJXDsbj8GQwY41qzh+9Aj+Xt7Enj2LWQjcnNyoV7kOwiS4kZuG1qRFqVRiNBrp378/SqUSk8lE2o00jCYjQghefeVVwiuHY2tly+WrxaXLpkyZAkBYSBh9+/TF2dGZ42uOA7B771aSTp9g3qD3i4W6Dok/7CRC4c3q976h1Rs9iZu+ArW31z33Kz8nh+s5WTj7VkZjZX3H8T3HdlHZpypx65NQ6K6hu3KNs4s3o1Vr2Bl7EAoMXI29RPdOPTmXeIYd3+3H1cmVSwdSOM95cgxZODk7Y6vK5GpBHlY25TCbBe+/dw1HR3sK8rU817UHO3Yc58KFyyxZ4s+5s+cwm01kZ2db5HBxcUGn02E0GomOjubXX38lLCwMgGnTpvHmm29iMpnIzMzEaDSiVCpxcnJCp9OhUqmIioqy+Hv36NGD//3vf/f/JZCRKUUkpYShwFDaYvwt97R4KkmSEjgKlAe+EkIc/FOTAcDyByzbHaSeOc23ET9gKiiuNnOImwgUHM84gwoVZozczLyJVaYVJkxc4zqON52oQAXLNUy3svUZjL/fGKPJyLGTxzBj5s+kJqRSQAGKQgVjO4ylIQ35kR9JJpnvVq9CiZICChjDGKwpVsg5FLFs1Fng94CV4xznKEeRkGhLW7zxLvE5e9hDAgmYMdOEJgQTzM/8zHWuo0WLESPlKc/CNXNYzGJ06KjxYnue4RkkJHTkkHA1mXa04SSxdOzXGhdcaEIT8shDkmBYh358/eMsikwmrutu0rSZLZcTVcQn5WAymZgw4VsqVarE9euZzP56GUuWfMVbb01Eq1VhNBbPBKyti/toMBhwc3NDoVDQuHFjUlNT+fLLLy3jGxgYyLlz5zCZTOTm5iKEQKfT0adPH4ti/+GHH1i1ahUff/wxo0eP/q9fDxmZR4JCpSB5ZzKX9lzCv5F/aYtzV+5JsQshTEC4JElOwM+SJFUVQpwCkCRpHMWlCJbe7VxJkoYAQwD8/f/9IJhNJn7quxgkNe2+DwN1AXkbT7Dru0wwg/EP1RA+fWYMtmYtXde9RJI6iXOGc5ZjNlY2WGutSc9KL3F9hVKBUlFcxuyP5ChyUCvUWNtZk+GdQVD3IAq+LqCeXSU+GzCW6esWs/X0AXqM+T0/z/yJCtq9YIMmKAiA7IJcln37HT+/NIUb2TrG/TiTRUM+trTfE3cc47arDA1scGuPkbrPtuTA8sMMiRxB9ZBwDpzex6Fz+6liZYvDlkKcHb15tV1f3l7xBVN6jGTb/zaRqL7MApuFKHKUhAVWYdqImaw7sJqClYUY9Aa+37aCXH3xOFmp1ezYkYvZrMB8a4FVo9HQvn17pk2bRmBgIEZjcVuNVmnJO/7HghCrVq1CkiTmzp2LQqEosTBarlw5AE6cOIFer8ff358rV66wePFii/KvWbMmp06d4p133mHgwIFyGTWZJ4KmHzXl8t7L/Nj9R55b/hz+DR9D5X4/dptiqwzjgdG33r8I7Ads7uXc/2Jj3/TpV2ICE8SWqd8IIYQwGvXi3XebWmy1CsXvttzqPpXEgWErBSBc7ZxL2HklSbrDHgwIlUp11/1WVlaifPnyQqvVinHjxok5c+aIGmFh4q0ePYQQQvTp00dUr169hKx/trGvX79evPrqq5btGjVqiMLCQsv2i717i6jQQBHu7yca1agjPurcSnz2fHvRsEoNUbtWLdGoUSPRv39/YWtrK4JcPURTWztRP6KuOH/+vLC1tRVnz54VCkkhXO09hZOjk1AoFMLX11c81+U5USmokrC2shaSJAmFQiFcnVz+MBaICuVdhVKpFIBo166d+GZO8Zg6Oan/cjz+uG1tbS0cHBxEVFSUZZ9GoxG//vqr0Ov1ln2urq7C1tZWKBQKAQitViuaNGkiAgMDha2trdi8efO//m7IyDxqrhy6IiYwwfIy6o0P9fO4Txv7P7o7SpLkfmumjiRJ1kAL4JwkSW2AsUBHIUT+/f+k3DvX4+LY904aAOnx17lyeD3HFoxGUpy2tDGbfw8cOJFyjuaLXixun1vSve6vigbfnp3+mcLCQi5cuIDJZGLz5s3odDpC/P1JTE3FaDSyceNGGjRocNdzb/NP5ccuJyYiSRKzXh7Kc/16seP8FQBalS9H3+qt+O6771i2bFlxKbaiIvbk5TJ18mT69u2Lvb09CxYsoJyjC3pDATm5OcXVfT76gPgD+7iZeYWCoiKEEJjNZjKzi8fDSqNFCIi/kG6ZQQ8bNowpkw8AkJl5dxtiYWGh5X25cuXQarVkZ2dbAnIUCgURERH069evRM3P/v37k5eXZ8mhotfrOXbsGJcuXUKv11O1atW/HUMZmccJnzo+DD0+1LL9mcdnpSjNndyLH7sXsF2SpBPAYWCzEGINMAuwBzZLkhQjSdI3D0vII9/utLw/O0/wbd1DrBnmyoqf21v2KxSKEkED7731LtJd4uiLiopQKe49Lkuj0RAaGorJZCIvLw9vb2/qVK1KStpNatWqhUKhYMKECQCsXLmSFi1acPXqVVq0aMG+ffuA4gXHvys/ZqdSUtGrHPYenrR/pj0KLx+a9h+NrVaDoSCeuLg4wsLCqFy5Mu80aY+dUsn8xYvo0aMHHh4e7N+/H1d7J0LcXHHQKtFIZiaOG0wzvzTycnKxcfNBpVRhY23D7RxUhUV6JAkaN/BHpSpOdPXss89y47oZf3811sHWWNurLCPo5OBUYlwkSSI9PZ38/HyUSqWlOpCdnR1r1qxBp9OV8AP/7LPPsLW1pVmzZpY4gZycHBQKBS+//DKenp73fE9kZB4HPMM9GVc4DoDCzEJSDpdO3dq78Y+KXQhxQghRUwhRXQhRVQjx4a395YUQfkKI8Fuvlx6WkC3ef5HWCyrT7KsAHKvmFQtuAtXNNG4nzP5j1jyAtyeMs0Rb/jlVqtF85+xcq7z7TP52bUitVkteXh5t27al0M4OrUc5UlJScHBwsHh2dOvWjS1btpCfn8+WLVuIjIwEoF69en9bfizc15crukzsff04cuQIlcIqUatNNCrHMBB6Fo9/m4SzZzgwfz4+hUZyTCYMG7bStl5j7OzsyMvMwpR9ne090xBmA0jw2YgRLDgBrtYSWiUo1RqMJiMqtRokCZWVLUJI7N5/CaPRiLOzMwqFAiutFampBqSrRgpyjJaUGPn5eShuBe1IkoQQwlLT0mQyUVhYiFKpJCcnh5CQEJycnLC3t6dx48aYTCaEEHTv3p01a9Zw4sQJGjZsyPfff4/BYOCLL764j2+DjMzjg0qrovfG4jKA8+vOL2VpfueJiDzV2mmp378bjYf34/WTU1B6FhcqMNSygb8I7RW/rwncofTvht709+5LhYWFZGVlkZ6ejl6v5/jx49jb2xMbG8s333xDXl7eX57r7Oz8t+XHWoQEcT07lz7j3mfBggW88847AKw6c5BZW/ex5ex5XG1tKGcncS43ESPwy40UajaojSnmJJqEBK7n57H3spGoyChMkpr31h8np9CAk7VEbasr6I1GigxGjCorEBLGwlvWs1s/fnl5OZjNBnLystBqFJhNJXNiTGr3Jh7lPLC3t7co9/z8fObPL/4yu7u707t3b4QQ2NjY8N1331G7dm22bNli+WFt1KgRFStWxNnZmWvXrjF16lQOHTr0j/dGRuZxJqRViOV97vVHW5T8r3jiSuMJIfhA9QGSWWK34zqOFJ0jqyD7Dy0kQKBSqbCysqLgVumb23ZkrVZL0S2b8x+xUmkpNJasOK9WazAYivh+8RK+nDmDTp064e7uTu7588xZvpyjZ84ghKBRo0YcPnwYjUbzr/q07fWXOZ6azIhFK9Ba25Q4ZjaZ2PbDYoaPG8+gulU55J7G3l/P8GmzBtiarVFIKmYfO8fRi4noi4pQabVEN6xKhxZ1GPHOPAIcTFz4UxS/f0hlUi9dwGD4vVanJIG3p4arqUV3TVynVqgxmA0WJW02m1EoFDg5OZGRkYGDgwMqlQqdTodCocBkMlGhQgW8vLy4evUqtra22NnZMWHCBJo3b86ECRMoX768XPRYpkywMGohl3Zfou2sttR9ue4Dv36ZL40nSRKSuXgmaShXHpMk0azOcygsdnOBRqVBrVSRm5uLyWSyKHX4XcHfno3eVlS3lbqfR5Cl7W3F9+m4GcQcj2XXhkOcPHoOlaSianAwFStWpEKFCgwZMuRfK3WAuOxiZbr74Mk7jimUSlr07s9b4yew+HAc6zefwXawF5+EXuRV42Hej4xjW/w5sgqKKDRBfoGeedXjGJL/Pyq7CXpVU/9ppUHi8vXrJZQ6gEopQaH2DqWuvjWuXvZuONo54O3tXWIBOicnB2tra7Kzs0lPT0cIQZcuXYiJiUGlUrFixQrs7Ow4dOgQ69atY+zYsbd85ifISl2mzHBp9yUAqjz/eNQVfvKyO5oMeHldJzXVgyo1MzmgrcDO5P0IOwfIz0FTox5Fx/aiUaipVqEy2bm5tIxuxsJli6lZqxbn489TWFCA/pZik5BQKpRICgmz2UyeIQutVoskFW8XFRWRdP0krep2Y/fRdZw+cwpJkriRdZlZX8+id+/eNGnShM6dO+Pj42MRU5hMSErlPXUpVhOEV2E2sbM/Ytc8e4oUWvzrNqL/gB4sX7mSuXPnIkkSi1etZEnSXLZl78OkEBReKuTyW5fBBIHlAwkP8+OX1bvpF1+OC2evcindzIldZgRQztuLjEIDhox0RG76HTIYjIIcw51zdaEAzHA5+xoAWbnZWFtbM2LECBYuXEh+fj4ajaaE/X3lypX4+Phga2vLiRMnqFy5Mmq1GrVaja2tLQkJCVSoUOGOz5KRedKxdX88Cqc8cTN2hCAnp3jwhkiJON28hEvjgdgE1EJl74ZIuohCoWDbtm2gVXIt/TrJaSlICgUxMTG4OrtgZ2+Pt7f37cvhVy4Ao9GIQqGwuOMVFhZa3PUi6kawZu8SrO01PNvlWQa2GEWQe3kmT56MKTcXkZjEqa5dufLaa1x5/Q0+9fKilp09NQNC+LDXbE4u2MzFjUctXZg8eTLNmzcnOjqabdu2kapwYd6Jq3y+9wQLt+5g/Y6NFOz5hXee68iQIUPJycmjsLCQAQMGML3rN8T0P4Epz0Ta6jQ0ajUKhQIfDx8Wr1gFQNzJyxQVmajfzNUyA79xNRWD7iYolCjDil0LHezsUP0hna+zqytQcrHZ+taPU3BwMP369UOSJAYPHszJkycpKioq9hKqU8eyUBwYGIharebMmTNcvXqVmjVrEhMTQ3Z2NikpKcTGxpZw9ZSRKUvMjZjLkW+OlH5a3/txev+vrweVBOzqxh3iQ8V7YmloX9G8U1uh8QoVSo2VeG3mUuHWaaxQKFWiqKhIJCcni4YNG4qMjAwREhIiJo77SNja2gobGxsR6BtQHGCjsRXj+30qFAqFcHd3F/b29kKpVIqgoCAxcOBAIUmScHNzE3Xr1hVeXl7i2rVr4oeB34mO1VoKjSSJqlorEWVrK8KtrEVNK2uxyNdPhGm1IqZCRbEpKFiEeFQWneoNEhW8a4oqnhVE77rPiXohNYWTjYPQqLQi2DVAuIZEiJDqUWL37t2iefPmIigoSFhZ2wg7WztR0auc+Oz59qKCn7+QJEnUqVtPdOzYSVQYWV5oPDRCUkiWAKvb7318VMLd3U306NHDEiD0wS/jBYriQKTQUNfiICHN3YOy1Orfg5OkW/87OTlZAplsbGwsicFGjx4tXn75ZaHRaCznlCtXToSEhIghQ4YIIYT48ccfRVRUlOjWrZuIiooSycnJD+R7ICPzuKBL0Ilvwr+xBCztmbzngV6fBx2g9Dji1aoJtV7Tcv58MBUPN6N684/o9Up/di36nJwjv2FX9zmqh9ckKiqKL774gtGjR5OTmc3Xi+diZ2dHiH8QamsNFUMr4uMaws+7VuDn50dsbCzz58+3LAiuW7eOyZMnI0kSx48fx9rampSUFNRaPdbWGga6uDDPz49zRUU41q6FMiSYd3NzqW1tw4y0NN7K13NZd57Em7uZ0G4MvZu+wy8ntnA48QTZhXm4O/piVtrg1mUCEipGjhxJamoq1atX5/q1VKysrejQuz+OzbujlECtUNDKw4EoKyMBp9UIs0DhoECSwNHFBkfH4vH5btl8VCo1y5cXp++xs7PjqyEzQVl8u69dzwSgkmc5XGytUSgUKBQKVKpiy9zZs2ctrowOVlbYajSYzSaLW2R4eDiBgYG4urri7u7Oq6++So0aNejWrRvbt28nICCABg0a0Lp1awC6du3Kzp07mTFjBjY2Nv8ptYSMzOOIc7AzQ44NsWwfnXP0b1o/fJ44r5jbmIwGfhi/gNOf38SqwEheeSccI7zZW5jNmQoeLO1TmYZVgwFI3H2axVtX4qF2pnbt2tRt04g54+az4rf1dGn4Mo0C7Xhx2RAOHjxo8S/v0aMHAwcOpHbt2jRq1Ag/Pz9mz55Nnz59iHCpwaEzB5inzGNpoAubdQYOHz4MQEBAAC2EluGOjtSMOYi3jzeSJOHv709gYCCnTp1Co1Lj7VaOrXt2YjKZcGzzOs83bM7Xb7elZ8+ebNmyBX9/fy5evEi3bt2YNWsW/v7+pKWl4enpTqv6TgSEZ/DxzHScmzhzffl1FIpis5IQMPerYGZ9Y0NObh6pqakUFemxtpbIyzMjKaBaVU9OnLiGQioO4TIJQUhICKmpqeTn52NnZ4dKpSIzM5PQkBC8lCYOJFzGNyCAnJwclEolAQEBqFQqS4bHy5cvEx0dTV5eHocOHSIyMhKlUkl+fj7Jycn4+/tjY2PDjBkzShQ9kZEpSyRuSeT7lt8DMCZtDDZuNv9wxr1R5r1ibqNUqen9yVBeix+B7ZCaKNILKFp+hnq/XOGFRbGUtym2w5/dHsOyLT9jo7DixZcHUrdNIwDUmV6cv3aKIAcF9p1c7ggaio6O5siRIxw8eBB/f38qVKhAUFAQKSkp7Di9m1da9EephG3HTqNSqWjevDl9+vTB1tYWndYKG0MuuqNnyMvLQ6vVsmHDBho3bkxmZibValRn/MSPCa9ZExtbO7IPrsTBqvgH9ubNmxQUFBAXF0eTJk345ZdfGDx4MLUbB1I+VM2ML9XsOH4Bd1cFjmYzfnonHNztMZuLlXr58mpWb7xB/PnTFORfpmrVYnNbXl6xL7+vlwcadfE6glkITEIgSRKvvvoqBQUF2NnZkZubS2ZmJjY2NtiolRxKvIKDtRYfHx8GDRqEwWAgLy+PhIQEvvjiC2xtbcnOzmbPnj1cunSJYcOGIUkS8+bNY9u2bSQkJLB9+3bWrl0rK3WZMk1wi2BaTWsFwFT3qehz9P9wxsPhiVXst/H0dWT0nI5M0r3FuMJxBA8PxeaGkQWtFrBryXZ+2vEbdiobBg8djI2TneW8Di/XJ6pKR0b+7xX6DHrxjqChfv36cebMGUaOHEl8fLwlaOjSpUtk5Wbx8ZqZ9L+eQkZeIUVFRWzdupV69eqRlpbGWd0NDEIwqGVD8vPyCAsLw9HRkTZt2qBWq7lw4QLvv/8+zZs3R5KUWAXWIjOlOO+Nk5MT/v7+bNiwgUOHDmEwGNi8eTMJp44xYoQzN686Y2/jyPXrRnp1d+HinnSMeSZcXV355JNP8PCoiyisQ+XyftxIM5F80UT16g58MvYNXFxcSElN48TJWCRJQqlQMLRzeyb26caxFUsQQjDgxf4IIahcqRLvt41Cqy/A1saanXv3smvXLg4fPoyjoyMnT54kNTWVrl27MmLECFq1akXjxo1ZunQpw4YNIz8/n9dee43o6Gh++umnR//FkGHRokV4eHigUChQKpVUqFDBUhZx+vTpBAQElPBmsre3p0qVKri4uGBra4tarbY8lfXr1w83NzesrKxwdnbG09PziSp1+Cip2LGi5b1Sc2+ecQ+aJ16x/xGNRkWfr3ri84IPRQlFbOu3E3WCiheH9sfZw7VEW4VSokGltozs+CV79+4lIiKC8PBwxowZAxQHMi1evJjp06fToUMHSy6TatWqMX3gx4x/9k0W+fgQ4mJPTk4OBoOB6tWrYzQY6GFjw4uXLpGk0PD68BHExcUxaNAgjhw5QosWLTh27Bjx8fHs3r0bF3cvilLj2LN5BZGRkRw6dIjg4GBq166NXq8nIyMDW1tbkhL1vP1WKnrzDQymbHy9NQxpP5y0tDRCQ0Px9fVl7dq1pKWlsWrNRrwCqmFjbYtW60FgYFOElRsdO3bE2soGTw8vXFxcUGs0uFUJR2E2YjAaUEoSMZvXEOrhxqWkRH46eorAkBBcPDypXD0cAKVSaSmsAdC3b1+GDh1KbGwsarWaiIgIrl69yvHjx/n888/57bffGD9+vKwAHjEZGRl89tlnZGVlUalSJcLCwrh69SqvvvoqACNHjqRbt24sXLiQtm3bEhISgoODA4GBgfz222+EhYVx5swZ/Pz88PPzo3nz5oSEhNClSxcyMjL47bffaN68uZxq+S5kJP7+Xb+4/WKpyFCmFPttBi0bxPPbnsfG3QrDchM3ttyZnCfvRnFEqsEsuHHq5l9e6255XjQqJaYiMBdJtG7WgOrVq9OkSRMGDx5Mff9QqikFLZ7pSmxGGp9Om4oQgmPHjrFgwQI0Gg0tW7ZEp9MRExNDfn4BWp/KtGvfgZUrV+Lr68vhw4fx8PDAw8OD06dPY2NjTaUwLR4eKt4cc40Xe75Nv77nCWs1nhEjRnDq1ClUSgVD+7/Im2++SZMmTbicfInBPfvj6eXJ6tWrmbtwDtmZefgH+FFQkE9ubh56vZ4T22NI0EFMThEmIdh74SK5hUUYzWYOX7zChWv5XL9+nZYtW1JUVMTevXupVKmSZXyGDBmCq6sr58+fx8XFhZUrV+Li4kK1atXw8fHBwcGB8PBwzp8//+BvtMxfcvDgQexvufW2bNkSlUqF0WgkOzvbklu/SpUqXLlyhQsXLpCVlYXRaMTd3Z2RI0dy+fJlPvroI5o1a0Zubi5OTk7o9XoOHDhAdHS0HGD2N4S0CqHrD10BWNp2aamYY568AKV7JKxpGL4HvFlQdz4r+v3CswVGKvatxrFFZ0g+q+Nm/u/RqAW6wr+8zh/zvEiSxIwZM9g/byP7Y2MJB/p27czRn3ai1WoJCAjgkz7DsP7kHb45H090dDQGg4Hnn3+eyZMnc+TIERo0aEDDhg0JDw/H3d2d8tGj+eHyTd5/vxV2Vmp+/ukHLqybhdXNUwh9Lts/H0hj9RnavheI2sbIiN5FtM+oy7aFP5OnL+D7xYvp/txznNt9ipGvjWXBd7NZu+xXFi1cREz8CWpXrcml5Cv06dOHixcv4uDgwLtjPqZu4+qEh4dz+tpZVmw9zfXr16lRowbLli1DqVDwzDMdqexXkwENJ1JrlgMfTHyX5s2bo9VqqV69umV8CgsLadSoEXZ2dri7u6PT6Shfvjz5+fmWqNQzZ84QEBDwEO+2zJ+5Xb5Qq9Xi7OyMo6MjGo0GtVqNTqfDy8uLFi1aUKdOHfLy8jCbzYwYMYIxY8bg5ubGzz//TNeuXfHw8KBx48a4ublx/vx5OnbsyFdffYWnpydz5swp7W4+tlTtXpXVg1dTlFvE11W+ptW0VlTp9uiiUsusYgewD3Ck59oezGu4gJWDfsXtg50UVfVB7edMhSAHbKxUhLUNwLWiy99eZ8CAAQwYMMCynbb6LG4Kb9hxgpsfT2fBb+tQeRUrrjnTZvBNcjJCoWD+jh1E1Pl9ITsiIoKPP/6YTZs2YTKZGDZsGD8cLkK39nMiVr2KgyENf+sCfnzehjyhRSkJzt3Mw9kajl82oLUNQ2uVSLzyMhFJYWxPPIgd1tgmwoIBk2k540V+nfITCW0ukafQk5eTx/mbCdjYWnH8WCyhFULYtm0bpy8c5YuvpxAQEIDBYECSJAICAujYsSNDhw7F1dUVH19vgsr7sCVmOfU7v82GDRsYN24cOp2Otm3bWvoUFBTE8ePHiYqKwsnJibFjx6JUKpk8eTJt27bFYDAwePBgPDw8HvDdlbkbJlMh8w+NIzlDQ26RQF9URGZmJllZWRQVFWEwGCwpo8eOHYuDgwMeHh5kZmZy5coV3NzcABg1ahT29vbMmjWLzZs3W0yEtra2HDhwAB8fH1JTUy0BfTJ30ntjbxY0XED25WzWDV8nK/YHiXuENy+feZlj0w5weHEMBetPo/F2JPDr9lToFPqvrikJM6g1uERXRLc9HrKugVcAGRkZfL1wPov9/blhMDBi6EscOPa7e+f69evJyspi69atACTHHyLnyD7cpAzmN7pGiL8XF306cr5+d8pXrYMkSbhs2EDG+vXctNqNZEoFBy1tP3gREGTNPY7ddluig+uR19KKcr95sD8tFj9FFcJcrPnm2EHS03XUrFmTqtUqI0kSzz3TixkzZqBV2uDm7EFuUS6VK1dm3rx5zJs3j06dOrFlyxasrKxo1TiAasEd+eTDyZx4eQs30q7j5+fHiRMnaN68Obm5uaSkpHD+/HmLD/xtWrZsScuWLf/1fZP5d8w7vYIJub1Qnz/P9as/ILKvWTJsqlQqHBwcLN5fWVlZlsynNjY2xYVYMjNxcnIiLy8PW1tbKlWqRExMDBqNhsLCQgwGA99//z0KhUJ+CvsH/CL9aPR2I/ZM3INPXZ9/PuEBUuYVO4BjiDNNv25L489a8GuXlZzeksBPw9YypuXLqGzunof978gTeq5LymKlDvzy+tuojUWcvJ5GSLoOjaMbvhoNJ2+cZshPw5nb9WsAVqxYgbOzMw3q1MNKe4XX39QQHVCZXEUyIzcWorRN49X3qtCgWl0WLVrE3LlzMZlMZGXq0FTT0M0czEW7DLRaLZMnT+aHH37gukHHes0xptfoyrn4c0gaJYvmf4Pa2p7U1FSmjnuN5Rv38fXXX6NSqWjdqjW1atVCyrUjLuk0455fzMuzm6NQSGSlFXBw/yG6NB1Ik5AXmL3+Hco1qEnd0BacSt1Feno6hYWFNG3alIMHD2JnZ0dSUtIDvVcy/w0H+3pwMx/7IC0FdTohHfmBM2fOIEkSPj4+lhz5HTt2xN/fn3379qHT6XBycmLcuHG88MIL5Obmkp2djbOzM8OGDbPY16G4ALnRaGTq1KnyU9g9kJmUCcD5decRt1yLHwVPhWK/jcpGTdcNPSlqvpT4bRc4MPsYjUbVu+/rnBYFKKTfy765ZqaSr1Vwk3wkOyW7qzqS7OMMGy9x4MpeS7uEuAtYFxrpWdOReNsE/vedkoWLvqd97QTsdg9HnIuj6dih+KrO8uWX2zhw4AApKSk0alyHjMn5/GhbxLfzf2DWrFls3LiR7du306JFCwoKc6lQOwwhwW9L3qJN+wkoVSpUKiUffrkQL2dbHOxsqVQhhBUrluPm7k5GRgbOth7MWvMmq45506f1SP63eg5FeiPPNR1ESC13EtRNcPDOpEolP+oYwuVEXk8APYMq8ryfkbzawTi+1hmY8bftZ8+eXWJ748aNf9l2ypQpD0DCp4vglsGcWnYKgLTTaZSrWu6RfO5Tpdhv03BCY+J3JrB19AYOzz/Ga6eH3lFl6e+wsnXFnKekaPNKFHlaIsr5YWOvQbl1Exs2bGDIrYpAM/ztKK+pypVzl9g89ztyriQSFhyAu18btE5pzN8fT172dfzK14Hyh8k58Q3ND45k0+yZ1HVxhLxEgoIqYVYVUf7NQPZ234KdvTdfffUV4eHhPPfccygUgi1HNmFUCr6dOQIvvy5MnjKl2FOnUUNOH91DwrUsdJk6ynn7ExB4lZSUFDQqJWobUFtJeDj58v3G6ew5vRFrG2umrBpGwNEArl27RgefDtRrEs6Y92LJzs4mJydHTuT1mKNSqXC8nV9CplRZO2wtUJzO172y+yP73DLp7vhP+Df2Z0jscKz9ncg+d4OPrT8laf991CuUQIGSGn5VqVYpFHsXK5RqxR2ukRqVFs8L6Swf/wq6lMPYOXmw7sIlZm7+iYup9fD2URN7aJylHurMNTq+Pa7m+zgl+WkZSF814JUXaqG7kc/5j5IIer4GAFevXmXTpk2cOn2ShIw4rCpZs+27D3lx4Ay+/fZbvvrqq+LHZKWKLftjObRpFW6ONhw6dIgxPZpy9J1w2jeoxHeLF3Eq4Sgfzx6NtbsZg9HA/v37cXNzIzc3l+DgYLy9vXFxceGDDz6gQ4cOvPHGG9SoUcOSHVNGRuavaf15cb6krMtZllKdj4KnUrEDeFVxY3TSK3g0CUIUmVgcOZ/VY7be07nFdrI7b9Jt18jIhg1o0qQePUMroT2Zx+HrBTzz9qfkKgVRTZpgMpmYPXc5nVt6kF8YxwsvvEDlypWZMmUKLi4u2LgGUhjYiEIHR6ZVuECos4Lg94PJOpJF/fr1OX78OK3aNKN8P0cKDSYyt2fTo29xLfGZM2eSlJTElStXmDNnDg0bNmTz4XMYUPP60BcZXkdLNeJoHh7E7bw9R44csYT6V6lShQ0bNrB69eoSHjByIi+Zf0vFihUt0a2enp6W6FcAT09Py7Hbr1GjRtG/f/8S+5555hkArK2tUSqVlteSJUtKq1v3xO5PdwPgXuXRzdbhKVbsUJx3/KUdfak2vNjOfuyzPXwgfcCmj3aTcTkbo/7OotcAihR7JPOdQ5eVoyNPf5YuFcoxom5tPGoFMHT6LJbt2ETStStERUWxdOlSDhw4QEBAAK4+JgoztWzcuJHw8HC0Wi2BgYEEBQWxbuthtEPPEhf2LI4qM+GH01BI0K37s9jYWvPNom9ISc2lvKMnAwcWZ5UrLCxk8uTJNG3alPj4eDp27IjRaKR3794YivR8980XVH9pLgD9Rn/CwYMHcXNzY/jw4aSmpgLFoeZNmzaldevWvPzyy7i7F38h+/btS9OmTRk0aBAzZ8584PdCpmyya9cu4uPjLdvXr1/n2WeftWzXq1fvDjPowoULWbRoUYl9a9euZebMmRQWFmI2my2vDz744KHK/1/JSckBoNFbjR7p5z6VNvY/03lmK7xqlGPXBzspvJrN/ve3sf/9bQA0n9WeRi+XTKpmUhlQGn73pinQ5/H9simkbT2MlV6B0d+WFn2HU6daU0sbnU5XIvzazs6KAkMGLu7F5pW4uDh8fHzYvn07s2bNYvfu3TSJbgZAkbohK77dD9Yw9q23KVfdHrWjlusrdBhc3Hj33XfZsGEDzZs35/Llyzg7O1OhQgVWrFjBkSNHqFOnDh6qXOyEhHuV6vD+UbQKBXq9npMnT+Ll5WWRa+TIkYwcOfKOMVq8ePEDGGmZp423337b8n716tV06NCBS5cuodfr0Wq1/PrrrwCsWbOGDh06ADB06FAmTZoEFD8d3/Ym+eO6gaurK+np6SQmJmIymVDeY7WyR0lh5u+Bj486Z8xTPWO/jUKhoMGQWoy5/BpRU1tT8cWalmNbR6zlQ+tPKMj6/SYpzEoMPpkYjEV8v3Iq0156npx1RzE5aan9xmDem7qyhFIHcHFxsdjSAXKy87BWazCpziKEQKPRULVqcWWjvLw88vPzMRqNdKzbmgEtxlPeszo2ZnsqekXgkhaGJkkJKKhatSqenp5cu3aN8+fPU1RURGFhISkpKVw+fxKvY9NRKwClBpNHdZ4b+hYoFCQnJ8uJumQeOhcuXACKTSi3zSnAHSkmBg4caHk/ceJEy/vbacWFEJaC6ADp6cXlHTUaDQkJCQ9H+P/IZOfJAPjW98XR79EuZsuK/Q8oFAqajq5P90UdGS/GEzW1eOFDFBqZ4jSZFYPWkJuWj2RSkHEjlonDu3Djx52YtQoqDX6ed7/4iej6ne567T8vrNo7OGInRaNySGPb9vK4u98kPv4cGRkZfPPNNzg5OfHZp+8yb/lKguvepEp9X5xdrFCocvBxUVHOsSLutuWwNplYtGgRBoMBg8FAs2bNMJmMdG4YRsM6NXnjy5/wcnPEOagGZqWWd999l8TERDZu3MiePXtITk6mefPmjB8/nsTERKKjoy0vOXufzH+h6NIl1LfMLH8OYEtOTi6xfePGDYAST49QskzjTz/9RGFhyfQfhYWFj6WHltlotrwfuH/g37R8OMimmL+h6ej6RI+qxxehs8hO0HH226Oc+/kE1q0TKGe6Tr49+PdqS9f2L6H4h0fBP+ec+fiDD1j+43KyspUMGmSid58iBg08Sr3aFSksVOLv68O5tSZSM5J5/6uJ6DKzsXdywWQyofJ05NK5HeQVFJKxJ5PNu/bh7e1F1WrViD20B0X2Dd4MuM6NK650GvI6R6fPZ+++fQC8+eabxMbGMnPmTGrWrMnBgwdp3749wcHB3Lx5kx07dgBw6NAh3n//fTl7n8y/4uyUH7DX+eJrF0AK18jNzS3hM1+3bl3L+08++cTy/tNPPy2hvCtWrMjZs2cBOHnyJCaTiYoVK3L16lVycnIs++vXr/+wu3TPmIpMLG5RbLoMbBpYKjLIM/Z/QJIk3rjwCkNODUcKc0LoDCQfrsa3LceQ9MzLpDrV5OilyxhNpr+9zqJFi5g/fz6SJDFz5kwqBAfi6elDxQo9iY1pxeZNuTg7K0lOSSM/P50WIS+xauePGIwFpKVncObMGSpXrkx8fDwnY0/wUc+ZuNh54OHog6PWnmspqcTv30JS8mW61XRl6KEgTqSrGTvxK+rXr0/z5s3p2bMnu3btokKFChQWFmIymcjJyaFWrVrExMSUCBFfsmSJnL1P5l9jr/MF4PnwYgUuhGD48OGW4xUrVrTUPvj000+B4r+1fv36kZSUhK3trUI5t5S6nZ0d/v7+SJLE+fPnycvLs1yrT58+D79D98HYTmN5f/f7rPRbSbXPqpU4Nn36dMsTcVBQEKNGjQJg//79REZGEhUVZRmXK1euEB0dTePGjQEqSZJ0zxWUnshi1qXF9dQMMaT6TEvBWu8Nh4XHtuPCY9tx4bvxoKj38ybR+5f1Ysq2XWLnuXiRry8UQgih0+lEzZo1hV6vF4mJiaJhw4ZCCCG+nPixGD9+vOjZs6do0qSeWP71QDH53VoiumoXMWvoVrFzw6+ievXqIjg42CJD9Qo1hJdzgAj2qCI+6v+ZCC0fKppF1BZX31srprYZIzQqtWhQr75o2bKlSE1NFa1atRIBAQEiMjJS+Pv7i+bNmwshhGjQoIGYPXu2iIyMFHZ2dqJTp06WzzAYDCIoKEjk5OQ8usGVKVOM2zxaDJ3cV4z8rLno2aO7UCgUxcXRJUl07dpVVKxYUVSqVEkIIUTjxo2FtbW1qFGjhuX8adOmidDQUAEIhUIh/Pz8xNmzZ0W5cuVKFF4fNmxYKfXw7pzZfkZ44ine5V2xe/luy9/63Wjbtq3Yv3+/EEKIiIgIS5H3du3aibi4OJGZmSmuX78uhBACOAXsFveoa2VTzH1QztOJObEjmDV5B+lv7aT3V2fp8Fl9DqVc40RBLvFCyU4bZzajhat5KC+fxDMvE9uj+9EGBLEt/gKRwYHk5uai1+sZ+sZoPp04kTNnzlAlpBLfrEjEkOZMUmos4Y2s8Asrj729PXl5eRRmFrLjy1gqezTF5JrH5zM+ZHfCDsLSwwgKCuJA9ULWrz5AiLM/Pzw7Dc8eVdF6OuDi4sL8+fNp0aIFFy5csBRamDdvHqNGjcLOzo7OnTuXSNi1adMmGjRoYFmokpG5X95p8gE/uyyilVMt3EfVZ+n/fvjLtrt27bpj3195Z12/fv2ByvmgWTJhCTbYsNx5OdtmbCMtLc3iAQTFM/bffvsNg6G4TnJYWBj169cnIyODDz/8kKSkJBISEli7di1vvPEGc+bMYcGCBQBBwN39r+/CE1vMujTJytHzhUOxO9Z4Mb7EMZPJROzlKxy4lEJMZjZxJokz+/dTcP0adv1eQjKbyXmtP43feJMIyZ3aabZMmv8KjTo1Y0zvsbwy4iOSbx5F6SIs+d87tutIo/IdaFL9BUIDrVl84itSUlNQq9V8+eWX9O7dm7NnzxISEkJy0kWquIcyo904QtvWYFnyJjKzs3jrrbdYtmwZ+/bt48svv7TIW1RUxLPPPsuiRYssPus9e/akT58+JdLzysjI/DVFRUUsW7aM6eOnc/LiSVy0LrgFFeewX7t2La1bt7Yo9StXrpCcnIwQgnLlylG/fn3WrFmDlZUVSqUSk8lkSaP9xRdfYDQaKSoqMgN64FMhxMf/JI88Y/8XONpr0Ttq0GYVUag3YqX9fRiVSiW1AgOoFfi7vXqd1sSyVatoYqUnJj2LRXm5nHf14rijC/OCIHNjOTJcyuOw6iyV/epwNGcPcz/7Gju1NTc3FmDQS7Sv3YNmPSrgF+lNKxqUkGf48OHMnz+ftLQ0AoICycnJ4qvV7zN2p5bGzm586qSlfPny6HQ6KlWqRFBQEAEBAUiSRGFhIfn5+XTu3Jn27dvz6quvsn//fhYvXoxOpyMkJISZM2fK9nYZmbuwYMECJk2aRGZmJkII0tPTUaBAp9eRlZCFyWSiQ4cOKJVKtFoter0ee3t7jMbiyff169dZu3atxasNitcarK2t+eKLLyyBWIAZOAqsuBe55MXTf4lZXTx0Myds+ce2DRo04ExMDH0jajGmeiVq+HhxoXMzDlbyY6GzG+3CG2ATf5Y0tURyWhx+5kDOLc7nyLfpJFzOR6NW0GCMBydStrD7/blsH/4FKweMoTCr2CtgwIAB/Pj5LKyy81iqcWC6JDiZloQmJBy1uYB348+zWqXm5pUr7Nu3j7CwMCZNmsT27dsJCAhg1qxZ7N27l23btnHlyhWSkpJQqVRMnDiRhg0bPtRxlJF5UsnIyGD8+PGkpaWRlpZGRkYGSqHEhAkzZouiNhgM6PV6DAYD1tbWJdwzzWYzRUVFJa4rhLAUjS8qKrr9I6ACGgGxkiT94yxLnrH/Sxot7MjRDivIn3SQ+IER3DCm8/yVHComnuaTpo0oFAIfR0cqerjftbxeTEwMmzdvZsyYMTQLe4v2vboxZ9VIlAolHjXDmLlnBFnXM3BVOXIz9wa12tbB41kPPrZ+ldjTp9mTfIzPqm7klUEDqHrdwMBF47lWVETvwkIGtOmC8fRBbGZ/TPdu3Si4VOwzfNzOjuPrN5CUlGRxD4uJibm96k779u3ZtWsXlSpV4tKlS6SmphIRce8L8TIyTxNbtmzh+vXrFgVu+hvPOCEE+fn5FBQUcK/m79tpPv6EFdAP+NskOfKM/V/yzDNhOH5QnP/hh9Cv6H0uE7NSxdnQGnS5kkPPlFyankxm+KrVfPzlzBKujhEREYSHhzNmzBhen/4htgEB7LpwFYTAdvgraGMOM+XTSSis4GzmeaxrWhM4KhCbUBveSf+co/anGdqkHt89/wnRBbVxyJJobmdPv+d6cuBaCjOP7sDB2QmAnXv2cCg/n0kffUR9R0f+178fz4SUR9z6Et6uoAPg5ORkiej74IMPGDdu3KMdVBmZJ4gtW7ZYlLrS2vaezvlzoNY/4eNz18pLQf90nqzY/wOvv98c9ag6APR/dgPqfAMfqvJ4T5nPh6o8QrLS+FHlyAczv6bxW+/x+qeTLF4pqflZPLPnVxZeM2LVrish0z6m25yPWeLjSpumzfnwww9pOaElGncNYTYV+NT9UzZ3WE/h/jxiiuKZtG8dfX56mf3XNpLMScprNeRbK9FoNCgUCrKzsy21LQF+SU5m0KRJrDebaXbmDNMaRNKgdm0uX75sybaXlZWFi4sLY8aMYd26dQwbNowZM2bwv//9Dygu7VenTh0aN25Mr169LHbCxMREOnToQLNmzejbt++jvAUyMo+cwsJC9uzex4G9h4t3SBLuAfdWTeqPE6l7ISXlrunE/zH/r2yK+Y+MndKGEesv4Hkmg2EfbWTIgXctxwabzbw5YxbfhlXhO3svvjNDYUoqYw6t44dsG4wKPzwVWynctR7X+FiCmzblZsWKODs7U6VKFfIM+UjWEtZKO7y9vPF08UV7w5qb51KxcddQzsqa2QeX0SSwPD+m3SRpxQoOnz5NQUEB169fZ+/evcTExLBq1SoOHDiAUqlECIHPx5/w6quvYA0Yi4po0KABL7zwAtevX+eLL77AYDCQlpaGu7s7RUVFxMfHc+jQId577z1++uknAgIC6NevH5s3b6Zt27aMGDGCb7/99o5wcBmZssjWrf/jxIkz5BZkFe8QgmvnEu/p3L8z19wHDv/UQJ6x/0eUCgWzT7+KbbAZwyEjRQV6yzFJoaCmhxuv1qrKlhA3nDNvku/gxKJLahSY+aq8mgNjPyAx7jw7d+4kOTmZ2NhYMjMz6du3L+vGraMwsRAbGxsiIiLIyMgg7cZNfAb48NGU97mQpuNKRhY/HD3KkoAAwmvU4MiRI/j7+7N161ZcXIpTEHh6ehIVFYWfnx+vvPIKV319wMsLlb09i318MRoMxB45SuPGjQkLCyM5OZk2bdrw0ksvYWNjw4QJE6hbty5VqlSxrP5nZWXh7u4uJxOTearQG3K5YL2awHp7KVfOurTEsJMkqfnfNZAV+wNCn2EGIVGYlV9i/+2sjlX9felxYhEOqZfpWhDDgchadPWvhrOzs6VoQPfu3cnOzmbPnj0MHTqU1hNaYx1kjaurKytXruTgwYN4BrhReKmQerUaUmA0olRIhLu5sTs3lwtxcbi4uKDRaJgxYwZBQUGMGTOG7du3Y21tzdWrV7Gzs0On03FTp8MhLIwVvj6ogcBr13i5YkWSk5Mti6b79+9n+PDhFlfHvn370qZNGypVqoRarSYiIoKrV69y/PhxPv/8c3777TfGjx8vJw6TKbM8c+wC0/Pb8unlXJSKi6UlRgGw7e8a/KNilyTJSpKkQ5IkxUqSdFqSpA9u7XeRJGmzJEnnb/3/VGeLMmYUW7WK8ktmn/tjVkeN0hF7YcT9+B68bZwASqTy3bZtGzVq1GD48OGkpKSw9+u9ePX0wmQ2sWzZMnQ6HTYBWvRX9Qzu8zL5hXqC3FxxDg7mzdSr5Obnk5OTg16vp2LFikycOJHc3Fx0Oh1eXl6MHj2aM2fOkJiYiNFo5Pjx48xauRI3Dw/25ucR98443mzVijeHvgTAvn37SvivDx06lEOHDhF36wdk5cqVuLi4UK1aNXx8fHBwcCA8PPyOlKwyMmWFtJTZOKQXF5qJzXt0pe7+xAnxD6419zJj1wPNhBA1gHCgjSRJ9YG3gK1CiFBg663tpx4Hz5K/b390dVy9PoZO4ZXJO6XjrffGADB16lTq1atHaGgoixcvZsmSJYSHh7N48WKEEKT+kErajTRq1KjBlClTuLj/Kk6NnJj73Uys1Gr2nE/ku42bOFqhIlFhlfD19WX79u20adOGadOm8csvv+Dp6cmJEyfo06cPrVu3vh3JRpUqVTCZTGisrQmpXp0TLVpgunkT7ejRXD18BEmSCA0NtfRFqVRasj26u7uj0+koX748+bd+UIxGI2fOnCmRTExGpizRzOV37xeF+pEbPAQwVQjR5J8a/qNkt/LV5N7aVN96CaAT8N2t/d8Bnf+VqGUFVfGiiEJdMn1vVlYWTaKi2LdvH4MHD2bt5Sv8fPwUObriFfVPPvmEDRs2oNcXYmNjjclkomnTpkRGRtLpy05o3DRcuniJ+fPnk5SUhL2tPVofLR3bd6HQaMLFxoYCo5F0o5FKfn6YTCa0Wi0ffPABvr7FGfZq165NXl4eoaGhHDlyhOrVq1OtWjWOHz/O888/j7u7O2k3b7L2agqpwcEMuZTM8g3ryU29xr7Nmy19+fjjj2nWrBlRUVHExsbSq1cvlEolkydPpm3btjRs2JDBgwcXF9KWkfmXDBo0yBJeX6FChRI1Ujt16oS1tTUqlQpJktBqtYwaNYqePXvi6emJVqtFoVBQuXJlTp06hbW1tcVTLCws7D/LNj16OiNci59o7WvY/23b2/VZHxDXgXPAe/fS+J68YiRJUlIczloe+EoIcVCSJA8hRCqAECJVkqRyf3HuEGAIULYLIBuLb+DSTt/y4rriG394107WbtsOgLu1li/nL+CzyWPJzJjC1C8OsHVbCDXrbWPH7gM4u6dTq4YVjZ634sOX9aw7M5grGen4DvGlypYqeLh4MHfuXDwdy3Hpi0v42XizbccOvv3gHRZs3MErKSkIo4EqVapQo0YNkpKSGDKkuBZqcHAwFy5cICoqitDQUDp37kx4eDjPPvssZ86cwdHRkXfeeYdhw4YBIEwmxnbujFtMDG7jJ5AjSdi3aEG3bt3o1q3bHV1v2bJliSRiMjL/lqSkJBYuXGhxCzx//jzdu3cvUTfVaDRavEuKiopYu3YtCoWCGzduWIJ/7O3t8fLyQqlUYjQaEUJw/vx51Go1N27c+E91Bn5ROTJ/hQkRb0Mj4K/8XEwmE66urpYiIv8RB+ANIYT+H1tyj4unQgiTECIc8AXqSpJU9V6lEULMFUJECCEibieZKov4NHYD4OLGa8X/nznNui1bLcf3nzyNk4sLaelb8A1SUFggKCoSHD/YjCMHX8XOSmLzplymj04mL8eEbfopIq2LFyGvpV7j4sWL9OrVi1otamAbZsvihV9Tv359rEOr4urszP8CAlj60mDWrl1LREQEBw8etGRn/PLLLzl37hy7du3i22+/RaFQEBwcTGxsLNnZ2Vy+fNmi1AEkpZIpq1fz6patqDw8uDLiFa6MHktBavqjGk6Zp5SZM2diNptRKpWsX78egISEBPT6Yn3266+/YjAYOHPmDFCcV2X06NEkJycjSRJOTk4AHDt2jD179uDm5oazszMqlQqNRkPz5s3/c/GYj32TcUgQOCqVDG4U+pftlEolubm5f3n8bqjV6r86dFEI8dcpMv/EfRmJhBCZwA6gDXBdkiQvgFv/P5CfpSeVXmv6Fb8xS3zUahSLVqxEKBSEerrTuGZ1PJ3sqRDoS0DQCfJzPfH2qYdO0QKddQPMLmFYeYSwYd+PHDiUgdloT1bWSMxS8U12di6OCO3VqxcR9WtSeKUQPcU5JNasWYONjU3xR98qTi1J0gN57LSqWIGg5csw1GrDt9NsmOI9iw+kD/i1/69c3n/5P19fRubP7N27F6VSiZubG23atAGKw/EvXrxYot3tQD+FQsGgQYPIz89HCIGvry8ajQaTyURCQgK//PIL1atXR6lUUlRU9ECS2TloJa59XMTx0dUIrPceDcoF39FGqVRSUFCAJEl3VdZ/VRzkdnrfP3IrPqTwjgN/w714xbhLkuR067010IJiW89vwIu3mr0I/Ho/H1zWsHawZfDJ4uEwb3bAulCiU5tW9Bw6nOadutDimU7kFB1BoTDj7dOHhMuXScmPpmX1mYwd8DOX44ro/62OsNHLyFXYMXvlb6g0NQAIrx3O2bNnadKkCUlnL6Ipp0EhKS1502/b8cxFBo4ePUpcXBxt2rRhyZIlTJ06lUOHDv3rfkkaDXu3FH/ZFKpiL4CYRTEsiFzA1ne2kn4+nYs7L7Ly+ZWseG4FcavjOLHkBMufXc759Xd6x1w5cIV90/ZhNpkx6u85vbTMU8afQ++zsrJKbG/fvh2FQkHNmsWF59VqNSqVilOnTlFUVIRarcZoNDJz5kwMBgP29vaYzWaaNPnHdcd/JCRkDBUjJ1IjpRXuv2QRdaMbtgorbG1ssLW1JTQ0lKVLl9KuXTtyc3OZMmUKLi4uODk5YWtrS48ePVi8eDFeXl7UqlWLatWq4eDgwIwZM9DpdHTv3h0rKytatWpFSEgISUlJUJzd8Z75x3zskiRVp3hxVEnxD8EKIcSHkiS5UpxC0h+4BHQTQvxtVdmyko/975haawr5xwuAkrnaT5xcR7fnnuXTiQ3wCPiEFp2GYFejFbkxG/ByMFGufAVijyWAJCGEGWP6FcyGfKz8tHipPFErNXh7e5N45QJX065hZdJSWKjH39+f1NRUrIuKcFCpuFRUREc/X8bVqMHcuDh8bW1od2sRFYA/3+9bmyYjSBIknHfh6lVvcguKnwKUCiMms4qKNcx0j/kAs8lM/Op4tozdQnr8P5tmnAKdkBQSjv6OCCFI3lmckEyhUmA2mgmMDuSZuc/gGur6H0b9yWfRokXMnTvXkk+oVq1almO383gDnDp1Co1GQ1BQEN27d2fx4sVYWVnh7+9PeHg4a9eupbCwkBMnTuDm5kZ0dDTDhg1j1KhRqFQqOnTowJgxxR5Zt4s4aDQaJkyYQPPmfxvz8vAwGeAjN/anVeTtLensjE/E2tqajRs3EhUVhSRJZGZm4uBQHHC5ceNG2rRpgyRJbNy4kZYtW9KuXTs2bNiAnZ0d+fn5WFlZ0blzZ2JiYqhZsyabNm0iPT0dg8GAJD0YN8WLEw/w3TsbsbJW4/ixE4t/XGJJ8qdSqSxJ/vR6PYMHD+by5cuo1WoWL16Mp6cnK1euZOrUqVhZWeHk5MT//vc/srKy6NGjB0qlEr1ezyuvvEKPHj2QJOmoEOKeM/LJhTYeAh+oPwAjNF3QlKj+UQDs3PkGq1cvYdeuAPKKCrgZ1ov8rbNwCY/CGNyS60vfwsrJgcperiQkJPD62NF8vX4V2SlnqOpWjflz5lGtWjV8A3wpKF/AyH79+WjwLAIDA3FxdqZ92nXOZeWQqNdzrqCA1ZUr80NaGgdzcjAhGOzpSR17e07k5fFFSgpKJBo7OtLXw4M8vZrp55w5Yj6FEiXRRBNMMFZqPYUGDSDR54dmBHdvbOmjEILLey+TeiwVQ76BCh0qkHoslazkLLzreHPu53MkbExAn6OnIL34h87KyYrCzDufKMMHhNPp206P5N48jnz11VeMGDHCsu3i4mJJxjZ9+nTGjBlTIseIQqFg/vz5DBgwwLJPkiR+++03duzYwbRp00pcv1q1aqxZswZ/f3/at2/P559/jpOTE23atOHgwYMUFhbStGlTDh48+CC9OO6d62co+jqaG/qfSM68SuM53RG3+iSEQKvVUqdOHTp27MiYMWOoVq2a5Qfutu09Li6OSpUqoVQqMZvNBAQEEBQUxPbt2y3XAahTpw7bt2+31FT9LxSl5PJLh2WcPZ5C5bahdFvX8z9f86+4X8Uu1zx9CCwfudxSF/U2GzZ2FmvW1hRCCBGTdE6U6/aBsK/dQRToC8UvB34VnoF+IvnaRbF+/Xrx6quvioULFwpH73JC0khi+OsjxPRD00XNxTWFtbtWODZwFF4+jkIhSaJf09ri7XZNha+zo6hXOVS89957wtPTU/z666/i7bffLiHXwoULha2trahdu7Y4evSopbbi5PrjhD32IsQuQIT5hAq1QiWG9RkohBBi9OjRIioqStSpU0eMHj3acq2EhATxzDPPiKZNm4o+ffr845jk3sgVBZkFQgghivKLhNlsFsl7ki3jlBqTKswm838e+ycNnU4nPD09BSB2794t/Pz8BCAKCwvvaBseHi4A0aZNG1G7dm0BiOHDh4sXX3xRAKJVq1bCz89PWFlZCZVKZakLGhoaarnG+++/L+bMmSMOHjwoevXqZdkfFRUl4uLiHkmf76AoX+ye01hcHrtLXB67S7RvH2aR38rKSgwbNkyEh4eLdu3aCSGKa6S6urqKrl27Wi5RrVo1S39VKpVwdXUVcXFxYty4cSI8PFxIkiQqV64scnNzH6joJpNJ/NR1hZjABJG4LfGBXvuPAEfEfehaWbE/JG4rrK+e+UqYzWaxZk0LsXZtIyFE8ZfBrcNo4diwhzgQd0AIUfyHdfXqVbF06VIxfvx4odPpxPBfZgqbCjaibcf2wmewj6i6qKqwCrQSCrUk7G20wkarFk0rhYjPnm8vbDRq8Vq/diI6Olp07txZ9OvXT7zxxhuiWbNmonfv3uLixYuiZs2aonz58paC2u+//7744LVxYhCDRHV1VYvszs7OYvny5UIIIfR6vWV/VFSUOHXqlBCiuBDv1atX//M4HV903DJWX5b/UiTvTv7P13ySWL9+fQklfPu1b98+IYQQHTt2vOOYJEkiIiLijv0ODg7Cxsbmjv3VqlUTMTExQq/Xi8jISDFx4kSRnp4uqlSpIrKyssSVK1eEo6OjpbByaZBXlCfe3v2emHzgY2E2P74/8GazWaQeTxUnlp4o8frE9hOxpM2Sh/a596vY5eyOD4nKYypzZuoZ0takMSl8EhXH5GHlWjzcF1LjUFjZYy7Mo25oXeD3lLm3c8s4OzsjKSTMBWZOB5yk8Fwh2igtyjQlC79diEqp5tNPPyVDrca3RzcUv20m4VoaaWn59O/fn6VLl+Lq6srWrVuZNWsWY8aMoXHjxhw+fJjs7GxycnJYv2YdDsccqU1tCv30ZGdnc/HiRbKysiwxBxqNBiiuAmNra4u3t3eJxF83btzglVdeoWvXrv9qnKr3ro4+W8+GVzegu6BjYeOFvGd8D4Xy6UhjdO7cOUv6YysrKwoLi01VR48epUGDBvz666906tSJTZs2WY5ZW1tz8uTJO66Vn59/1+yBbdu2ZezYsQghCA4OxtvbGxcXFz744AM6dOiAh4cHNWrUwNvb+yH29O+xUdvwaaMPS+3z/4kbp28QszCGMz+eISs5665tLmy4gNlkfiy+u6UvQRml25RuvJX/Fh7dPCg6redk31bozxcRu+QrdEc/QuNdEe2NExiNRi5duoSdnR1ardaSWyYtLY1Lly6hsFKQdz4PjaeGXmG9sLW1JSnxIi4uLhQWFuLs7ExBpqCgyMjWnbEUFBQwbdo0hBAWd7E2bdoQHx+Ps7Mz8+bNY+zYsVy+fBlDcgH22OPopuHTKRPp0KEDL774Ir6+viX+yF955RWCg4Px8vLC0dHxgSb+UigV1HulHq4Vf188zb12f76/TyoH353LD7OXW7avXr1qeT9z5kxLxOX7779fwibs4+NjsS3/EbPZfNfqPDVq1GDDhg2sXr0anU5nKVLetWtXdu7cyYwZM7CxsSnbAYT/gsLMQo58c4T59eYzu+psDn55EI9qHnSY34HhZ4YzIm4EI+JG0Gt9L4JbBlO5W2UkRanljymBPGN/iGittby04iV2zdzG9jd2ceK1tpzgJhpXP8q3NWCo2vmu5fKGDx9OzZo1uZGeDo6ulC9wo3vYC7xR+w08Pvfg1VdfZfr06eTn51v82M3CjLOjLSaTiaKiIpo1a8aRI0do0aIFR44cISAggMzMTKpUqcKGDRuoXCGMnPOFhBLK6ORxaGys6Nq1K7Vq1SIgIKDEH/nMmTP5/PPP6dq1Kxs2bCAkJMSS+AuwJP6qW7fuvx6r/rv6M7/+fDKTMpldbTa1h9amxcQW//kePK6smRVD8s3yWBl+X6z8Y33ZiIgIunbtSlJSEosWLSpRJ9PNze2uida0Wi0FBQV37F+wYAHz5s0DYMyYMdwOFOzbty+XL1/GxsaGmTNnPrC+PckIsyBpexIxC2I4u+osxkIj5aqVo/XnranWqxq27ncuurpWcKV8m/KlIO1fIyv2R0DUK80Ib1+TLcPXkHohkZsJNkQvSUDVoxHj1n1eom14eDhQXKD65R9/Ye0RNb++VZNAp+IZdM+ePenZs3j1fcGCBQwYMIC0tDTKOdljMpt5rlMnZsyYYXGxatq0KVeuXMHNzY1t27aRn5/PyZMnib9wHgccWOf9G0kvJmFtbU1cXBxnz54lNDSUunXr0r17d4YPH46VlRUqlQpbW1tsbGxKJP6ytrZ+IIm/bMvZ0mN1D+JXx5O4JZG9k/ZyculJ/CL9aDWtFQ4+/1hb4InCbC42mTi4hkLSXgDOnj1rOb53714uXbqEXq+3hMRDcaj8vn37sLW1JT8//0/XNGNtbX2Hcg8ICODbb7+9Q4bFixc/0D49yWRezCRmUQwxi2LISs7CysmK8AHh1BxQE69aXg/MRfJRISv2R4RDsDNdNhRHm636cAcnx+8k/+ezFOmNaLT/7jYMGDCACjWq8kL7FxjZ4hmyDFp2Hj0KFM/eFi9ezPr169m9ezeffvopCxYsYP78+cUpBUQwYYRR96U6jHuvuOrT2LFjSUxM5Pjx4wghqFy5Mjt37iQrKwuDwUCjRo2Ijo4GsCT+MhgMDyzxV7kq5ShXpRwNRjZg79S93Dh5g9PLT1OYWUjvDf89YvBxoopyMMkFTelcoz5rjiwC4PXXX+fLL7/EbDZz4sQJOnTogE6no3PnzsybNw+TyYSzszPVq1fHYDCgUChwdXUlLS0NKJ7Je3l5ceTIEZo2bcquXbswmUyl55/+mGMsNHLmpzPELIghaVsSSBDSMoQWk1pQqXMlVFZPrnp8ciV/gunyfjRHNl5Auy+FiVafoOoexttLn0OhKLnkcXuWcDe7KcCuI4dZ/v42KrjXRKVU4aZxJjcpF71ebwlNXrFiBc7OzjRv3hxvb2/Wr1+PyDUQ7duCIxwhddVVylcI5YUXXuCTTz7hwIEDlkrq1tbWrFy50rKA+kceZuIvpUZJ1Lhi/3+vWl5sGbuFDa9voMn4Jlg7l1rVmgfKtIDKdLlUlz3WZwkLC+Ps2bN88cUXQPF9DwkJ4ebNm7i4uPDJJ59YFkU///xzunTpQlRUFLt377YodUmS2LFjhyWycvv27ZbPql279qPt3BOAqcjE3NpzSTuThnOwM00/akqNvjVw9HcsbdEeCPLiaSnx8e4B5FcpXjA0LjvLW37T2bLqzF3bCgSFhXp+2rSR739cwxczlvHVS9s4OT+HPH0OtiFKlGoFNg4aHB0dS9hjr169ikKhYOvWrdSrV4+JEyfyVZUvaEUr3ggZxIatG5k0aRKJiYmoVCratWtHxYoVqVChAkOGDLmrUn+URI6OJKR1CAdnHGRG4AyOLzyOMD+6oLqHxacNP+Na4jI0wPRJGtq0ccTW1tYSfDRhwgScnZ3RarXs2rWLxo0b4+vri5tbcbK58ePHEx4ejqOjIxEREaxZs4by5cuTkpLCyJEjLUE9Y8eOpWLFiqXb2ccQhVqB1qF48vPK+VeIejeqzCh1kGfspYZCoWDyqRGYjGY+7vID6s1J7O26km22aozlbFD7O3DVWuDlaMN3q37BKs4Nh+xygBo1Npbr+Da1xemmD1JGFiAsbpO3cXFxKeEdM7jvQKKzmuFTQ8uw/W+isdbSsmVLYmNjMRqN/PTTTyQmJlryanTu3NmySFoaSAqJXut7cWX/Fda/up7fBvxG/Op4Wk9vjVOgU6nJ9V9xcvakTfPWpPz8OTejjIwe48pXX7/Gju12zJ8/H0mS2LRpk2VBfdeuXZw+fZqhQ4eiUqkwGAx8//33VK16Z6LVadOm3RF9KvM7CZsSSNyaSEZiBp7hno+NJ8uDRFbspYxSpWD8b73ISM9n6cRdXDuWCldzMR+9Ro1cAzUA1W5HVF21uLTRExjoRa2qlTlwLBaDyUDPSsNo2bIlAWFV0OVkW9wmbxMdHV3CO0adduuAk0BjraWoqIi9e/fy4osvIoTA3t7ecr5Wq73vtKMPA0mS8Iv0Y9DBQSzrtIxzP5/j3M/nqN6nOs8ufra0xfvXuL30Ei5DBrFzVzhmcwF+vn0YMMC5RKoA+H1BvUqVKuzZs6cUJC0bGAuNbB67mUNfFifFc63gSpf/dSllqR4Ocq6YxxSz2cwvi45ycuA6AIK6VqHjF61w8r3TO2TBggV89NZbKJRqlq/+9a4JiJISksi+pKPllbbYY89S7WLca3tiMBjo3bu3JQ3q22+/zfbt2xFC0LRpUyZNmvRI+30vpBxKYX69+QC8mvAqzsFPdbldmX/AqDcSszCG3Z/uJvtyNvVeq0eLSS2eqMVROQlYGePEL/H80mMlovBWiluFhENFd4QQjDg6GI1Nca7nL18cgNbWlaFfT73rdb6JmsT13b8HtfTc+iyhzao/dPkfFjGLYvi1/6+EdQ3j+R+fL21xZB5DhBCcXnGaLW9uIetSFr4NfGn2cTOCmgWVtmj3zf0q9ifnJ+sppXrnClTOfouY5Wc4OOsw+qxCss8W1zT5KmI+g3e9iJ2bDXB3O6HZbOaH3rO5vluPbYiZZh+1oFaPxndt+yRRvXd1jn97nLM/nWV+vfl0+V8XXEJc/vlEmTKPEIIr+6+w/b3tJG1LwrOmJ8/MfYaQViFPnD/6v0VW7E8AKrWSiN7ViOhdzbLvt1FbOD59L9Pcp/L8xj6YiiSM0p15Qia6jceYUXybh+x5HQfPsmG2UKgUvLjjRY7MPsLWd7ayZewWeeYuw824myzruIz0+HSsXaxp93U7ag+p/Vjkb3mUyIr9CaXjtBZc2p1M+uErrGj9PeAHwC+Oi+g8ox8AF3aetCj1sVljsHKw+YurPZkolArqjqjLtdhrnP3pLMIsyqSHg8y9IYRg2zvbSI9Pp82MNtQcWBONbem665YWsmJ/ghlxaCDnNiayZfwO0g8W1yCN/TKZ2C8/QGlvxJRTfHubz6xT5pT6H/Fr4Mfx+cfRJeie+kpMTyvGQiOreq3i7Kqz1H2lLvVerVfaIpUqsmJ/wqnUOphKrYuL6W6duIo97xSnc1VowWww4h3lSOTwNqUp4kNHbVu8gHzz3E1ZsT+FFOgKWPn8SpK2JtHys5Y0GNmgtEUqdWSvGJknnsyLmcwImgHA86ueJ+zZsFKWSOZRIITg3C/nWD9iPXk38uj4bUdq9K1R2mI9FO7XK+bpWlGQKZM4BTpR99XilMEruqzAeNs1VKbMYjKY2DhyIyu6rMDa1ZpBBweVWaX+b5BNMTJlgrYz2mI2mDky+wib39xM2y/blrZIMg8Ys8nMqWWnODbvGNdPXKcwo5DqvavTaWEnFCp5jvpHZMUuU2Zo/Xlrkncmc2jmIZpPbP7UekSUNQwFBja8voFjc4srSrlWcCWsSxgVO1akwjMVZE+ouyArdpkyg0qrou6rdVn70lrOrDxDeL/w0hZJ5j+gz9ZzePZhDkw/QN6NPACeW/4clZ97fErQPa7Iil2mTFGtZzXWvrSWhI0JVO1RFdW/LGIiU3rkp+dzcMZBDs08RGFmISGtQ2g8rjEBjf9bla6nCflbL1Om0NprqdytMqeWneLS3ku8nvz6UxNG/qSTnZLNgc8PcOSbIxjyDIR1CaPR243wjvD+55NlSiArdpkyx3PLnmNR6iIu7bnE/9r/j17repW2SDJ/gdlkJmFjAkfnHCV+TTxIUK1HNRq93Qj3yu6lLd4Ti6zYZcockkKi77a+THaezOW9lzEVmVBqlKUtlswfyLmaw/EFxzk2/xhZyVnYlrMl8s1Iag+pjXNQ2chnVJrIil2mTKJUK2n2cTM2vrGRj7Uf021lNyo/V7m0xXrquXLwCnsn7yXutziESRDcIphWn7WiYseK8o/vA0RW7DJllnqv1WPnhzspzCgkOyW7tMV5qknalsTuT3aTtC0JK2crGoxsQO0htXEpL6dafhjIil2mzCJJEr039mZ+3fllogD2k4gwC37s/iNnVp7BzsuOlp+1pPaQ2mjttf98ssy/RlbsMmUaK0crkODa8WulLcpTx8UdF9n4xkauxVwj8s1Imn7YVHY/fUTIcbgyZRrXCq40GNWAE9+f4MyPZ0pbnKeG/Jv5LH92OQUZBXSY16G4xqis1B8ZsmKXKfM0/6Q5AMfmHytlSco+2SnZ7PxwJ7OrzcZQYOD5H5+n1qBacizBI0ZW7DJlHqVGSXi/cBI2JjCn1hzyb+aXtkhljrQzaazqvYovAr5gx/gdeFT3oP/u/nJwUSkhPxvJPBW0/6Y9KCBmQQwruq6g0duNCG4Z/NTVwnzQ6HP07Bi/g4MzDqKyVlH/9frUGV4H52DZF700kRW7zFOBSqui4/yOuFZwZd/UfSxtuxRbD1u6LO1CcPPg0hbvieTcL+dYN2IdOVdzqD20Ns0+aoaNW9ktwfgk8Y/TFUmS/CRJ2i5J0llJkk5LkvTarf3hkiQdkCQpRpKkI5Ik1X344srI/HskSaLR2EaMTBlJtx+7obHTsKLrCrIuZZW2aE8UhnwDq4esZvmzy7Fxs2HgvoE8M/sZWak/RtzLc6gRGCWECAPqAy9LklQZmAJ8IIQIh/+3d+/RUZXnHse/z8xkcpWQSIKBBAgoYOROEBHQilGrVkUtXlovq4Jy6lmoxwtU5VCtRV1CtbpwqVystNLWJXpELaIgokIhJCBKINKEi+ESJSEJQmKu854/ZjMkkIRJmJnNhOez1iz2vPsyvz1knuy8+529mWE9V+qU54p0kXFjBjcsuoGG2gYWXrIQT73H7lhhoWRrCfPOn8fGeRsZ/bvR3J1zN6kXpNodSx3jhIXdGFNsjNloTR8C8oHugAE6WYvFA/uCFVKpYEgdmcr4N8ZTvqOcDXM3UHu41u5Ip7SCpQXMHzmfqpIqbvv4NrKeycIZoZcBOBW16cyRiPQChgLZwAPALBHZDcwGHm1hnXusrprckpKSk0urVID1v74/XQd1Zel/L+XF9BdPu2+oTpo0CRHxPSZMmOCbd9111zWZ1/fqvkw/PB33g276XtnX1+5yudi2bRvJyclNlk9JSbFxz05zxhi/HkAcsAG4wXr+EnCjNX0TsOJE2xg+fLhR6lRTW1Vr5pw7xzzBE6ZgWYHdcUJmx44dxu12G8C89dZbvuljbVm8xcQQYwBzxWVXmJgY7/TMmTNNUlKSAczYsWPNl19+adxut7nqqqvM4MGDTWRkpA171TEBucbPWm2M8e+IXUQigHeARcaYd63mO4Ej028DevJUhaWI6Ahu/+R2AJbdv8zmNKGzaNEiHA4HTqeTm266CYfDWw62b9/uW6ZoTRGv3fIa1VQDsOyTZdTV1QHgcrno168fABUVFaSlpQGwYsUK8vLycDq1m8Yu/oyKEWABkG+Meb7RrH3Axdb0OKAg8PGUCo1OqZ047+bzOLDtAN9/fXpcV6a4uBiPx4PL5R31HBnpvTDXJ5984ltm9dOrWelcCQ5wu92+5ZxOJ9OmTWP16tUAdOvWjX379uF0On1HjTU1NZSXl4d4rxT418c+GrgdGGcNbdwkIlcBdwN/EpGvgaeBe4KYU6mgG/XgKABeH/063286cXGPjo729Seff37TP1hdLleT/uYjX6mPjIxs0lZSUsKePXuatEVHRwd+55qRkpKCw+Ggvr4egJqaGgAuv/xyjDGsfnY1BUsLKPQU4vF4mDJlCgBvvvkmDQ0NAL6j/K1bt5KYmEhUVBSdO3dm5syZGGP44osvQrIvqil/RsWsNsaIMWaQMWaI9VhqtQ83xgw2xow0xmwIRWClgiVleArnXHUOdZV1vDb0NfZk72lx2QULFlBdXe17npOTw7333ut7fqTwNRYTE0NtbdORN8nJyUydOrVJW3V1NRkZwbspiPF4eGvaXCKL6/F4PDQ0NLBkyZIj59Lo06cPX878kk8f/ZTqkdXUG2/hnz17NuA9qQqwadMmEhMTERHi4+N5+eWXqaqq4qyzzuL666/HGMOQIUOCth+qZfp9aqUsDqeDW5bcwiVPXQLAX8b+hd3/3t3sspMnTz66nnXU+sorr/jaevXqhYjQu3fvJm3NycvLO64tPz8/aN0Y9Qd2sS2qmMokyBzUE4Dx48dTU1NDZmYmXZO6kvW/WQz81UBWR6ymoaGBTp06+dY/MlpmyJAhlJaWIiI89dRTzJkzh5qaGjZv3kz//v1JSkqiZ8+eQdkH1To58ls6FDIzM01ubm7IXk+p9vpxz4+8cfEblO8op0v/LvS+vDeDbx/su6jVka4VEaG6utrXP717925SU1N93SqNP19r165l1KhRx73WyJEjyc7ObtImIng8wfvSVOnapeSsXM2Vjz/dpL3upzrmDp9L7aFafrv5t0R1jgpaBuU/EdlgjMn0d3k9YleqGZ1SO3HrB7cS3zOe0m9LWf/SeuaNmMdffjaf8v27fMtFRET4TioCfPbZZy1us6VuiZtvvvm4NofDEdQTj11GXXVcUQdY9ftVlOaXcu2Ca7WohzEt7Eq1ICkjiQd2PcDEtRN9N8Iu+nwvk6/6yLdMfX19k771xn3jMTFNr52yc+fOZl9n5syZx7UlJydTUBDagWZl28tY98I6hk4cSp/L+4T0tVVgaWFX6gRSL0hlwtsT+L35PTgaGFj4I+7u3gLu8Xia9K2PGDEC8Bb1ysrKJts599xzm93+4cOHj2vzeDwh75/+bPpnON1O3zkGFb60sCvlp5r6apwRTjwHq7nsnAebXcYYQ3JyMjk5OU3aZ82a1eJ2L7300uPaSkpKiIuLO7nAbVD8VTF5/8zjggcv4IyUM0L2uio49HrsSvkp0hXFiHkjWXdHNuIx1DbU8rc3/sb8+fMREV588UVcLhfLly8nIyMDYwwzZswgKSnJNwa88cnUVatW8eabbzJ//nzAe+Q+cOBACgoKfF8aCpXVz6wmMj6SCx++MKSvq4JDR8Uo1QaHig/xfLfnyXz6Eq5+9CK74wREWWEZc/rN4cKpF5L1TJbdcVQzdFSMUkFU+FEhABkj02xOEji5r+YiTmHkfSPtjqICRAu7Un6qOlDFh5M/BKBTWqcTLB0ejDF8+9639M7qrX3rHYgWdqX8FNU5ivRL0wGY03cO+3LD/94ypfmllG8vp9+1/eyOogJIC7tSfjpyyYEhvxkCwDeLvrE3UABse38bAH2v6WtzEhVIOipGqTZwRbq47vXr+H7T9+z8tPkvHIWTXat2kTwwmU7dO0bXkvLSI3al2qGhpoED2w7YHeOklW8vJykjye4YKsC0sCvVDiVbS3BEhP/Hp7qimujE0Fz/XYVO+P9kKhVi1RXe67CfNfgsm5OcHGMM1RXVerGvDkgLu1Jt5I5zExkfye5/72bDvPC9v0x9dT2eeg/uM9wnXliFFS3sSrWRw+XgnlzvnSA/vOdDfir7yeZE7eOKcuGKdlFVUmV3FBVgWtiVaofEsxMZ8+gYAF5Ie8Gve6SeakSEhPQEKnZW2B1FBZgWdqXaadzMcfzitV9QV1XH36/+OxW7KuyO1GYJvRMo215mdwwVYFrYlWonEWHYpGGMnjaaqgNVrJy+0u5IbZY0IInS/FLqfqqzO4oKIC3sSp0EcQhZz2Yx4t4RbF60mf15++2O1CZpo9Lw1HvYlxP+l0dQR2lhVyoAhk4cCsCqJ1bZG6SNel7UE3EKBR+F9jZ8Kri0sCsVAMnnJTPs7mHkv5NP1YHwGWUS1TmK3lm9yftHHsYTunszqODSwq5UgPQf3x8g7K4hM/DXAzn43UF2/3u33VFUgGhhVypA+lzRh8SzE9k4b6PdUfxmjGHLW1uAjnG1SuWlhV2pAHE4HaRnpbPj0x1hM8rEeAwF//L2r/cY3cPmNCpQtLArFUCpF6SCgVcHvcqBglP/6o8Op4Pzbj6P2ORYBtw6wO44KkC0sCsVQIPvGMytH9xKWWEZKx8Lj3HtGb/MoHJ/Jd998Z3dUVSAaGFXKoBEhNRRqQBsXbyVNc+toWhNEQd3H6ShrsGWTJMmTUJEfI8JEyZQ8V0F6+esZ+yAsQy4aQBP8AS9x/X2XmYgIYG5c+ficDh868TFxVFSUkJ6enqTbYkI5eXltuyXapneQUmpAIvqHEXK8BSKNxSzYtoKX3ts11jOn3I+dZV1DPnNEM4858ygZ5k1axYLFixo0rZ48WLG5I5h6a6lfMVXGJoOc0xJSWHy5MnHbeuaa66hqKgIt9tNdHQ0Bw8exOl0kpCQENR9UG0nxoRu7GpmZqbJzc0N2espZZfaw7XsXb+X6MRoNszdQO4rx//cZz2XxehHRgctw8svv8x9992Hx+MBoFtCN/aVe79hOoYx1A6sxX2Gm+ycbOrqjp7sdTgcvnUai4mJoaqqChEhMjKS6upq4uLiOHToUND2QXmJyAZjTKbfy2thVyo0KnZVIE5hz7o9LL5pMQDdR3Zn4tqJYLyXJwiU8vJyhg8fTlFREQ0N3i6gCCKow1vAH7rxIWYvns0PP/xAWlpak8KekJBARUUFx9aGcePGkZOT06SQR0dHs3fvXj1qD7K2FnbtilEqRDr36gxAfFo8MStj+Ou4v7I3ey9/cPyhyXK/XvZrIs+IJO3CtHa/VnZ2NgmdO/ND8Q9UNXi/CetwOaDeO//Gh24E4LHHHqO+vt63ntvtxhjDmWeeSWlp6XHbrKysBODOO+9k4cKF1NTU8NVXXzFu3Lh2Z1WBpydPlbJB+iXpPPzDw3Qf2f24eYt+vojXR79O4bLCdp1w3fVTDQ8vz6Zmz484qo9+xOvN0QI+bNgwCgoKyMvLa7JuXV0dycnJvq4Yt/vo3ZUGDRqE0+kE4L333gPA4/Fw+PDhNmdUwaVH7ErZJDY5lknrJvmel+8o58P/+pCdK3diGgyLrlwEwGWzL+PChy484faMacDjqWdi9nI835bRUA4NHP3FcKRLJjY2lvT0dPbv30+fPn18/eUigtPp5P3332foUO9FzWpra33rZ2VlsW7dOgAOHjwIePvj4+PjT/KdUIF2wiN2EUkTkc9EJF9EtojI/Y3mTRGRbVb7c8GNqlTHltA7gds/uZ0Z9TN46PuH6DqoKwDLH17Ok/Ik5TtaH1b49Td3s+rzDGI9RZxddw4/1lcSQQQJnY/2f6elpTF27Fj69OlDcnIy27dv93XFJCYmUlJSwvTp030FXUTo0qULcXFx7N+/n27dugEQFxfHFVdcwcKFC7n44ouD8Xaok3DCk6cikgKkGGM2isgZwAZgPNAVeBy42hhTIyLJxphWL0atJ0+VapvCjwtZ9PNFzc6L7xHPwaKDdD+/OxGxEezbnE9taTSR3Q9TszeOjT03ssl8TXl5Genp6UydOpXc3Fy6devGI488wowZM0hKSmLKlCmcffbZFBYWAnDRRRdRWVnJG2+8wcCBAwFYvnw5d911F2VlZURERJCZmcmSJUuIjY0N2XtxOgv6qBgRWQLMAe4G5hpjVpxgFR8t7Eq1nfEY1jy3hg1zN7Tp/qR3fHMH6QPTgxdMhUxQR8WISC9gKJANzALGishMoBp42BiT05btKaVOTBzCmN+NYfS00dT8WEPt4VoObDtA8VfFDLh5AK4oFxExEbiiXeQvX8Hyh5aTcWsfLeqnMb+P2EUkDvgcmGmMeVdE8oCVwP3ACOAtoLc5ZoMicg9wD0CPHj2Gf/edXo9CKaXaoq1H7H4NdxSRCOAdYJEx5l2reQ/wrvFaD3iALseua4yZa4zJNMZkJiUl+ZtLKaVUO/kzKkaABUC+Meb5RrPeA8ZZy/QF3EDpcRtQSikVUv70sY8Gbgc2i8gmq+0x4HXgdatLpha489huGKWUUqF3wsJujFkNtHQRi9sCG0cppdTJ0ksKKKVUB6OFXSmlOhgt7Eop1cFoYVdKqQ4mpDfaEJESINjfUOpC+A67DOfsEN75Nbs9NLt/ehpj/P4iUEgLeyiISG5bvqF1Kgnn7BDe+TW7PTR7cGhXjFJKdTBa2JVSqoPpiIV9rt0BTkI4Z4fwzq/Z7aHZg6DD9bErpdTpriMesSul1GlNC7tSSnUwYV3YRWSCdSNtj4hkHjPvUREptG62fUWjdreIzBWR/4jItyJyY+iTty97o/nvW1fVtEVbs4tIjIj8y3q/t4jIs/Ykb/fPzHAR2WzNe8m6lLWtRGSwiKy1cn0gIp2s9ggRWWi154vIo3ZnPVZL2a15g6x5W6z5UXZmPVZr2a35PUTksIg8bFdGAIwxYfsAzgX6AauAzEbtGcDXQCSQDmwHnNa8J4E/WtMOoEu4ZLfm3wD8HcgLl/cdiAEusZZxA18CV4ZDdmveemAU3qucfmRX9mP2Iwe42Jq+C3jKmv4V8E9rOgbYBfSyO6+f2V3AN8Bg6/mZjX/2T4VHS9kbzX8HeBvvrUJtyxnWR+zGmHxjzLZmZl2H94e7xhizEygEzrfm3QU8Y63vMcbY8q239mS3bk/4IPDH0CU9XluzG2OqjDGfWevWAhuB1NAlPqqt2UUkBehkjFlrvJ/cvwLjQ5e4Rf2AL6zp5cCRvzwNECsiLiAa770Sfgx9vFa1lP1y4BtjzNcAxpgDxpgGG/K1pqXsiMh4YAewJfSxmgrrwt6K7sDuRs/3AN1FpLP1/CkR2Sgib4tI15Cna12z2a3pp4A/AVWhDuWn1rIDYP0fXAN8GrpYfmkpe3dr+th2u+UB11rTE4A0a3oxUAkUA0XAbGNMWejjtaql7H0BIyIfW5/Pqbaka12z2UUkFpiGt0fAdv7cQclWIrICOKuZWY8bY5a0tFozbQbv/qYCa4wxD4rIg8BsvHeICrhAZheRIcDZxpj/EZFeAYrYogC/70e26QL+AbxkjNlx8ilbCBHY7K3uUzC1th94//J8SURmAO/jPTIH7193DUA3IAH4UkRWBPP9bk47s7uAMcAIvAcvn1o3cQ7pQUA7sz8JvGCMOXwKnII59Qu7MSarHavt4ehRAHiL+T7gAN4fmP+z2t8GJp5UwFYEOPsoYLiI7ML7/5YsIquMMT872ZzNCXD2I+YCBcaYP59EtBMKcPY9NO02OnafgsaP/bgcfPccvtpq+xWwzBhTB+wXkTVAJt4ugpBpZ/Y9wOdHukdFZCkwjBD/ddfO7COBX4rIc0BnwCMi1caYOUEL2oqO2hXzPnCLiESKSDpwDrDe6iP9APiZtdylwFZ7IraopeyvGGO6GWN64T2q+U+wivpJaDY7gIj8EYgHHrAvXqtaet+LgUMicoE1GuYOoKWj/pARkWTrXwcwHXjVmlUEjBOvWOAC4Ft7UjavlewfA4OsUVQu4GJOsc9nS9mNMWONMb2sz+efgaftKupYgcL2AVyP97d8DfAD8HGjeY/jHdmwjUajGICeeE9+fIP3SKBHuGRvNL8X9o6KaVN2vEe5BsgHNlmPSeGQ3WrPxNu3uh2Yg/WNbTsfwP3Af6zHs0cyAXF4/xLdgrcoPmJ3Vn+zW/Nus7LnAc/ZnbUt2Rst8wQ2j4rRSwoopVQH01G7YpRS6rSlhV0ppToYLexKKdXBaGFXSqkORgu7Ukp1MFrYlVKqg9HCrpRSHcz/Aw3FLz/YcT1PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.8 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[p] == 0 :\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7c1c15c4-38fe-4f60-9a62-b791eb6ea329",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prep to read back in\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "#tractGeomFile = gpd.read_file(\"state_map_files/fl_pl2020_t.shp\")  #Boo!  Only has geometries.  do not use\n",
    "oldTractPopFile = gpd.read_file(\"state_map_files/tx_pl2020_t.dbf\") #for Texas, need only this file\n",
    "#tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #COULD UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"TX37HD1tol0.005nW424MarnoElP.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractHisp = df2['tractHisp']\n",
    "tractBlack = df2['tractBlack']\n",
    "#tractVAP = df2['tractVAP']   #add this back in -- but need to save tractVAP next time\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "69499dab-67d8-4434-99aa-f7061b8a5128",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TX , true number of districts = 38\n"
     ]
    }
   ],
   "source": [
    "STATE = \"TX\"\n",
    "nDistricts = 37 +1  #adding panhandle back in as a counted district\n",
    "print(STATE,\", true number of districts =\",nDistricts)\n",
    "minX = -106.2 #actually -106.22 is the western cutoff for non-ElPaso districts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1b9107b-3849-4326-891c-3c65a51a625b",
   "metadata": {},
   "outputs": [],
   "source": [
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "HDweight = HDweight.to_numpy()\n",
    "#tractVAP = tractVAP.to_numpy()   #ADD BACK IN, PLease\n",
    "#stateVAP = np.sum(tractVAP)   #ADD BACK IN, PLease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d07d793f-e3aa-423e-a40b-c35922ae995a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nDistricts,true state Pop are 38 29145505\n"
     ]
    }
   ],
   "source": [
    "#recover the original tract populations, compute true total state population\n",
    "oldTractPop = oldTractPopFile['P0010001']  #this was the population of each tract before we cut out elPaso and clipPoly's\n",
    "statePop = np.sum(oldTractPop)\n",
    "print(\"nDistricts,true state Pop are\",nDistricts,statePop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "27f1bbba-08fb-4bb3-8bc6-e31076f88b4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_53/3627159054.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  HDweight[t] = tractPop[t] / statePop  #true for both elPaso and rest of state; renormalize on true total pop\n",
      "/tmp/ipykernel_53/3627159054.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  HDvGOP[t] = panhandle_vGOP  #all elPaso tracts assigned the elPaso R-D lean\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "panhandle restored to main HD list.  total HDweight =  0.997261841920392\n"
     ]
    }
   ],
   "source": [
    "#Now, correct partisan stats for elPaso district.   (WILL ALSO NEED TO DO THIS FOR RACIAL STATS AT SOME POINT)\n",
    "panhandle_vGOP = 0.31857859 #see panhandle calcs in block32 when elPaso was created\n",
    "for t in range(nTracts):\n",
    "    if tractCPx[t] < minX  : #this is likely an El Paso tracts\n",
    "        if tractPop[t] == 0 :   #will be true for el Paso tracts that were wipe out, but not near-elPaso that were untouched\n",
    "            tractPop[t] = oldTractPop[t] #restore to original (we had wiped out elPaso)\n",
    "            HDvGOP[t] = panhandle_vGOP  #all elPaso tracts assigned the elPaso R-D lean\n",
    "    HDweight[t] = tractPop[t] / statePop  #true for both elPaso and rest of state; renormalize on true total pop \n",
    "\n",
    "print(\"panhandle restored to main HD list.  total HDweight = \",np.sum(HDweight) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "58719e38-0d10-455c-be9b-b67082cb5e8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#run this block only if whole map was a multipolygon\n",
    "counter = 0\n",
    "for geom in wholeMAP.geoms:\n",
    "    if counter==0:\n",
    "        fullMAP = geom\n",
    "    counter+=1\n",
    "x,y = fullMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()\n",
    "wholeMAP = fullMAP  #resetting full map to exclude the Keys\n",
    "fullMAP = \"toe fungus\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of tracts that were used less than 0.7 of expectation in TX\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "print(\"map of tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.2 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.2\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 0 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAwFElEQVR4nO3dd3gU5drH8e+dCqF3CBB6FZASOoh0AaVJEaSIAoqgRyygWDigxwOKCCp4xAooIEhTRCkq0hRMkAAhhN57JwESkjzvH7vkBSR9dye7e3+uKxezMzszvw27d5595pkZMcaglFLK/fhYHUAppVTmaAFXSik3pQVcKaXclBZwpZRyU1rAlVLKTfm5cmeFCxc2ZcuWdeUulVLK7YWHh581xhS5c75LC3jZsmUJCwtz5S6VUsrticihu83XLhSllHJTWsCVUspNaQFXSik3pQVcKaXclBZwpZRyU1rAlVLKTWkBV0opN+XSceAqe/hsy2ccuXTEIdsSkaxvg6xvAxyTpWHJhrSv2N4BaZRyPi3gXub8tfMM+WGI1TGyrfIFyrPv2X1Wx1AqXbSAe5nEpEQAPurwEcMbDLc4DTjqhiKGrG9n4JKBrDu0zgFplHINLeDKUo7o9gDHdMP4iI/D8ijlCnoQ08s4oqWqlMoetIB7KW1pKuX+tIArpZSbSrOAi0gOEdksIhEiEiki425Z9oyIRNvnv+PcqMoRHHXQUCllvfQcxIwDWhljYkTEH1gvIj8BOYEuQC1jTJyIFHVmUOVYjhp77Wn0D5xyJ2kWcGN7R8fYH/rbfwwwDJhgjImzP++0s0Iq5Qr6R025m3T1gYuIr4hsBU4Dq4wxm4DKQHMR2SQiv4tIfSfmVEopdYd0FXBjTKIxpjZQCmggIjWwtd4LAI2Al4D5cpehDSIyVETCRCTszJkzjkuuMkWHESrlOTI0CsUYcxFYAzwAHAUWGZvNQBJQ+C7rzDDGhBpjQosU+cc9OZVFdBihUu4vPaNQiohIfvt0TqANsAtYArSyz68MBABnnRVUKaXU7dIzCqUEMFNEfLEV/PnGmGUiEgB8ISI7gHhgoNFD+Nme/hcp5TnSMwplG1DnLvPjgX7OCKWcT0dc3J0eI1DuRM/EVMpOjwsod6MF3MtoC1Mpz6EF3Etpa1Mp96cFXCml3JQWcC+jo1CU8hxawL2UjkJRyv1pAVfqFvoNRbkTLeBK2em3EuVutIB7GR1GqJTn0ALupXQYoVLuTwu4Ukq5KS3gXkYP0inlObSAeyk9YKeU+9MCrpRSbkoLuJfRUSip09+PcidawL2UjkL5J+1WUu5GC7hSSrkpLeBeRkehKOU5tIB7Ke0uUMr9aQH3MnqQTinPoQXcS+lBzLs7evko9WbUY9yaccQlxFkdR6lUaQH3MtoHnrLaxWsT5B9EXEIc//793zT4rAHbTm2zOpZSKdIC7qW0D/yfnmn4DLFjYtnx9A6W9VnGqZhT1P+0PlP/nEpiUmKK652KOcWLK19k/eH1LkyrFPhZHUC5lvaBp0+nyp3YPmw7A5YM4LkVzzEzYiZTHphCcJ5gRq0aRcSpCILzBFMufzlmb5sNwHt/vEeRoCL0qdGHqR2mWvwKlDfQFriX0j7wtBXJVYTlfZczp/scjl4+SouvWlDpw0os3rUYX/ElNj42uXjn9MvJMw2e4czVM3yw+QPazm6baqtdKUfQFriX0T7wjBER+tTsQ+vyrVm5byXxifGUyluKtuXbIiJ8u+Nbwo6H0a9WP+4tfi8vNXmJkCkhrN6/mm8jv6Vvzb5WvwTlwbSAeyntA8+YormK0q9Wv3/M712jN71r9E5+XDpfaSKfjuSe6ffw/IrnaVGmBYWCCuHn40fUmSjKFShH7oDcroyuPJgWcC+jfeDOV71IdXL45eBU7ClKvV/qH8tHNx1Nj+o9KF+gPAVzFgRs34z2nN9D3sC8FMtV7LYuriSTRJJJws9HP67qdmm+I0QkB7AWCLQ//ztjzNhblr8IvAsUMcacdVZQ5VjaB+5c6wetZ2bETHaf203t4rU5cvkIgb6BrD20lokbJjJxw0QEoVlIMxqUbMDvh34n7HgYAMVzF+f7R76nfsn6hB8PJ/TTUACahzSnepHqlMxTkhx+OXhr3VtULFiRzYM34+vja+XLVRZJz5/0OKCVMSZGRPyB9SLykzHmTxEpDbQFDjs1pXKYJJMEaBeKs9ULrke94Hp3Xbb52Gbm7ZhHkkli2e5lTN00lSqFqjCp7SR2nNnBV1u/osFnDXi+0fNM/nMyYCveF65fYPGuxZyOPZ28rS0nthCXGEeQT5BLXpfKXtIs4MZ21CvG/tDf/nPze/j7wChgqVPSKYeLT4wHIMA3wOIk3qtByQY0KNkAgPfbv0+SSbqtBT0sdBi9FvRi8p+TqVG0Bn1q9GFM8zHJy0/FnOJEzAnqfFIHgOiz0ZTNXxZ/X3/tX/cy6epUExFfIByoCEwzxmwSkc7AMWNMhH4ddx83Em8AWsCzCxHBV27v/mhQsgF7ntnD+WvnKZa72D/WKZa7GMVyF6N28dpsPbmVujPqJi9b2W8lbSu0TX589PJRqnxUhas3rtKnRh8mtJlASL6QDGXce34vZ2LPEJ8YT8NSDcnhlyODrzJrjDHEJcaluN8kk8SE9RMoX6A8HSp2IF+OfC7NZ6V0FXBjTCJQW0TyA4tFpBbwKtAurXVFZCgwFCAkJGNvHOV4N1vg/r7+FidRqfH39b9r8b5V+NBwFkct5sjlI4xcMRKA2BuxAJy4coLgycG3PX/ujrn8dvA3+tfqT5JJYkSDEZTNXxaw/WHfe34vMyNm8mDlB2kW0ix5vUofVkqeHhY6jOmdpic/jjoTxZdbv6RDxQ6EBoeSJzBPll73TZGnI4lPjGfV/lXMjJjJnnN7eKz2Y4xtMZbgPMHJx3CuJ1ynykdVOHzpn7248a/Fe/z7XDI6LlhExgJJwDPAVfvsUsBxoIEx5mRK64aGhpqwsLBMRlWOsP7wepp/2ZxV/VfRpnwbq+MoB5FxtoLWuFRjHqz8IK/++mrysugR0VQuVJnmXza/7XT/GkVrsO2pbTT6vBGbj21Onh/oG8jGJzay8chGdp3dxbS/pgFwb7F7iTgVQYOSDSibvywTWk9g4JKBrDu8DoDyBcqz79l9qea8Odpm68mt5M+Rn/Zft8ffx9b1065CO5qFNGPejnlsOLIheR1/H39uJN24bTsPVn6Qs1fP8ufRP5Pn/TLgF1rPap38uEKBCvSv1Z/H6zxO6Xyl0/27zI5EJNwYE/qP+WkVcBEpAtwwxlwUkZzASmCiMWbZLc85CISmNQpFC7j1fj3wK61ntWbNwDW0KNvC6jjKQbad2sbEDRP57cBvnIg5QZl8ZXiizhO8dt9rt7VWw4+HU7VwVR6a+xB/HP2DsvnLcvDiQcA2/PHElRNcuH6BwXUG89nfnyVvP2p4FFfirtByZsvkVv7d1Cleh08e/ISaxWr+o8vDGIPP+Luf/F0idwli4mO4En8led7zjZ6n1z29qF28NueunePN399k0a5FJCQlEJwnmKK5ilI6b2l+P/Q7X3T+gpblWpKYlMiM8Bk8vfzp2wr/i41f5N1272bqd5sdZKWA1wJmAr7YTr2fb4wZf8dzDqIF3C38vPdnOnzTgY2Pb6Rx6cZWx1EOZozhdOxpiuYqmupQ0S0ntjB101Ri42NpUroJzzV6Dh/xodOcTizfs5xZXWcxYMkAAHYN30WVwlUAiImPISEpgYiTESzetZh6JerRvVp3luxawo97fmTujrnJ+3ig4gMs7r2Ydza8w4KdC6gfXJ8vt34JwNut3iYkXwg5/XPyQMUHCPIP4tL1S6zav4q6JeqSyz9Xml1IaUlISmDw94OZGTHztvnNQpox7+F5lMxbMkvbd6VMF3BH0gJuvR+if6DzvM6EDQlLcZib8l5rDq6h5cyWyY+X9F5Cl6pd0r3+xiMbWRy1mNgbsXwc9jHl8pfjwMUDBPkHkcMvB0WCijCt4zRal2+d9sYcZNPRTTT6vNE/5vuID31r9qVn9Z50rNQxW58olVIBz76JlVPoQUyVmvvL3k/40HDmbJ9D9SLV6Vylc4bWb1K6CU1KNwFsY9cHLBlApYKVWNFvBeUKlHNG5DQ1LNWQG6/fYO2htazct5KJGyYCttErX2/7mq+3fQ3YupBCg0N5q+VbbtNnri1wLzNvxzz6LOxD1PAoqhauanUc5eHOXztP7oDc2WrYqjGGI5ePMHbNWBZHLeZS3KV/PGd53+XcW/xeFkQuIPpcNMevHKdQzkJ0qNSBw5cOM6LBCJe+Ju1CUQDMipjFwCUD2fvMXioUrGB1HKUsdzLmJNM2T+OXA7/wx9E/0r2eGeu62qldKArQE3mUulPx3MV5s9WbvMmbAAxcMpBZEbMI8A0gb2BeCgcVJsA3gAoFKpA/R/7kA7HZgRZwL6N94EqlbmbXmczsOjPV56zev9pFaVKnd+TxMnotFKU8hxZwL3PzxAYt4Eq5Py3gXuZmH3h2HvOqlEofLeBe5ub1wO+8Ap5Syv1oAfcyyTd00EsAK+X2tIB7mZv3xPQR/a9Xyt3pp9jL6C3VlPIcWsC9zM0zb7ULRSn3pwXcyySZJG19K+UhtIB7GYPR/m+lPIR+kr1MkknS7hOlPIQWcC+TZJK0Ba6Uh9BPspcxRrtQlPIU+kn2MnoQUynPoQXcy+hBTKU8h36SvYwexFTKc2gB9zLaB65U1t28JIXV9JPsZbQPXKmsyU6fHy3gXkb7wJXKOlfeDD41+kn2MtoHrlTWZKfPjxZwL6N94Ep5Dv0kexntA1fKc2gB9zLaB66U59BPspfRPnClPEeaBVxEcojIZhGJEJFIERlnn/+uiOwSkW0islhE8js9rcoyvZiVUp7DLx3PiQNaGWNiRMQfWC8iPwGrgFeMMQkiMhF4BRjtrKDXE65z6folDCZ5CM/NwfTGmBSn05JSazSlfuLs9vyMrnMt4Zr2gSvlIdIs4MZWBWPsD/3tP8YYs/KWp/0J9HB8PJuIkxHU/qS2szbvdcoXKG91BKXclp+PH8euHGPJriV0rdrV2izpeZKI+ALhQEVgmjFm0x1PeRz4NoV1hwJDAUJCQjIVcuvJrcnT0ztOR0SSW5E3W5+CpDidkpROh02p5Z7dnp/ZdWoXr53iMqVU6kY0GMH6w+t5eP7DPFXvKUY1HUWZ/GUsySIZOaPI3s+9GHjGGLPDPu9VIBTobtLYWGhoqAkLC8twyGd/epYPN3+Ir/iS8EZChtdXSilHuhx3mYfmPsTaQ2sBeLPlm7x232tO25+IhBtjQu+cn6GjWcaYi8Aa4AH7RgcCDwKPplW8s+Lhag8DkGgSeWfDO8TEx6SxhlJKOU/ewLz8/tjvjGk2BoDXf3udE1dOuDxHmi1wESkC3DDGXBSRnMBKYCKQAEwGWhhjzqRnZ5ltgYOtH/zRRY8SeSaSGkVrUK9EPWoXr03ve3pTIk+JTG1TKaWyatrmaYz4aQQAZqxz2rEptcDTU8BrATMBX2wt9vnGmPEishcIBM7Zn/qnMeap1LaVlQJ+0w/RPzBgyQAuXr8I2P4SLuuzjOZlmmdpu0oplVnlppbj4MWDrOy3krYV2jp8+ykV8PSMQtkG1LnL/IoOypYhD1V5iFMvnsJHfNh5Zie9FvSi+/zu/P3k35TKW8qKSEopL1e3RF0OXjzID7t/cEoBT4lbntER4BuAn48ftYrVYukjS7mecJ2+C/uSZJKsjqaU8kKLohYBMLLRSJfu1y0L+K2qFK7C1Aemsu7wOnot6EVCko5SUUpZ4+Owj4k+G+2y/bl9AQcYVHsQ/27xbxZGLeTN39/MNhdbV0p5h13Dd5EvMB/vbnyXqtOqMvJn17TEMzQOPKsccRAzJcYYHln4CPMj51O3RF2G1x9O35p9yeGXwyn7U0qpW12Ju0LeCXmTHztyRIpDxoFnZyLCzK4z+eTBTzh39RxPfP8EBScWpPzU8gxaOohtp7ZZHVEp5cHyBObh4L8OJj8e/P1gEpMSnbpPj2mB38oYw28Hf+PH3T9y9MpRluxaQnxiPMv7LqdDpQ5O379SynvtOL2Dmh/XBGDvM3upULBClrfp8S3wW4kIrcq14r327/Ftj2/544k/CPANoOOcjnSd15XLcZetjqiU8lCHLx1Onj5+5bhT9+WRBfxOdUvU5cjII4xqMoplu5fR7ItmHLp4yOpYSikP1K5CO6Y+MBWA+766jzOx6TpRPVO8ooADFM1VlIltJ/LToz9x4OIBqk+vzrTN04iNj7U6mlLKg/j5+PFsw2fpWKkjAEUnFWXQ0kFO2ZfXFPCb2lZoy499f6RknpKM+GkExSYVo+/CvpZciEYp5bk+7/w5FQrY+r+/2vqVU/bhdQUc4L4y97H7md2sH7SeLlW7MG/HPKpNq8aus7usjqaU8hDFcxenS5UuyY+dMWDEKwv4TU1DmvJN92/Y8uQW4hPj6TG/B5euX7I6llLKQ1QuVDl5+sDFAw7fvlcX8JtqF6/NS01eIvJMJPkn5mfgkoF6NqdSKsueXv40YLuNYdn8ZR2+fS3gduNajmPD4xvIE5CHWRGzqPxRZT7c9KHVsZRSbuzRmo8C4Cu++Ijjy60W8Fs0Kd2E86PPM6ntJPae38uzPz9Lsy+aMTtitl4kSymVYbO3zQZst1xzBi3gd/Dz8eOFJi8w4N4BAGw4soEBSwbg/6Y/Mk54efXLWsyVUmm69TR6Z91I3CNPpXekDYc3MDNiJp9u+fS2+eULlGffs/ssSqWUyu5knABQOKgwp188jYhkflvedCq9IzUNacqMh2Zgxhpix8TySI1HANh/YT8yThj580g94KmUus2+8//fuMtq8U6NFvAMCPIPYu7Dc9k+bHvyvCmbpvDSqpc4e/WshcmUUtnF/gv7qfih7Y6T/+v0P6cVb9AulCxJSEqg4gcVOXTJdl2Vsy+dpVBQIYtTKaWscjr2NMUmFQNgVJNRTGw70SHb1S4UJ/Dz8WP7sO082+BZAFp81cLiREopK72y+pXk6RebvOj0/WkBz6I8gXmY2sF25bHIM5FcuHbB4kRKKav0vKdn8nSRXEWcvj8t4A4yuuloAMb8MsbiJEopqxy5dMSl+9MC7iB9a/YF4H/h/2Nx1GKL0yilXC0xKZGhy4YCsPSRpS7ZpxZwB6lVrBbD6w8HoPv87oQfD7c4kVLKlW69726hnK4ZzKAF3IE+6vgRK/utBCD001BW719tcSKllKvULl47+donzb5s5pJ9agF3sLYV2tI8pLltenZbPe1eKS8hIrzT9h2X7lMLuBOsHbQ2+ULui6IWWZxGKeUqeQPzJk+74hybNAu4iOQQkc0iEiEikSIyzj6/oIisEpE99n8LOD2tG/H39QdgVsQsi5MopVzl1lEoN5JuOH1/6WmBxwGtjDH3ArWBB0SkEfAy8IsxphLwi/2xsvtu53cAlMpbyuIkSilXmR85P3k6wDfA6ftLs4Abmxj7Q3/7jwG6ADPt82cCXZ0R0F0VzFkQgHyB+SxOopRyhf+s/Q/j144HYFX/VS7ZZ7r6wEXEV0S2AqeBVcaYTUAxY8wJAPu/RVNYd6iIhIlI2JkzZxwUO/sb1WQUAO9sdO1BDaWU6/194m9e/+11ulfrzrlR52hTvo1L9puuAm6MSTTG1AZKAQ1EpEZ6d2CMmWGMCTXGhBYp4vxTS7OLW6+DsPbQWguTOJYxhnc3vEvnuZ3p9m03pvw5hcOXDlsdSynLHLt8jOHLh5M7IDefPfRZ8rdvV8jw1QhFZCwQCwwB7jfGnBCREsAaY0yV1Nb1tKsRpmVR1CIenv8wAMeeP0ZwnmCLE2VekknCR3xYsmsJ3b7t9o/lT4c+Te8avckXmI+FUQv5++TfPFrzUQrmLMhvB36jSekmPFj5wdsurRl5OpJNxzbxWO3HuJ5wnSD/IFe+JKWybPpf03nu5+cQEWZ3m02ve3o5ZT8pXY0wzQIuIkWAG8aYiyKSE1gJTARaAOeMMRNE5GWgoDFmVGrb8rYCDrBw50J6LOgBwLVXr5HDL4fFidJ2Je4KEaciOHTxEJP/nMyWE1sA29UXE5ISKBxUmKMjj7L3/F42HtnIG2ve4GTMyXRtu26Juvj5+OHv48+GIxtuWxbgG8CmwZucdvsppRyt1ORSHLtyjBX9VtCuQjun7ScrBbwWtoOUvti6XOYbY8aLSCFgPhACHAZ6GmPOp7YtbyzgAF3mdeH76O/J4ZeDa69eszpOmqpPq07U2ah/zO9atSt/HfuLsS3GMqTekNuWbTu1jYiTEZy/dp7GpRsTGx/LhiMbaFCyARuPbOT76O9JNInsPb+X6wnXKZqrKEH+Qey/sJ8g/yCu3rgKQJ3iddjy5BaXvE6lsmpWxCwGLhlIoG8gu5/ZTUi+EKfsJ9MF3JG8tYAnJiXi96YfANEjoqlcqLLFiVL2SdgnPPXjU/j7+PPrwF+pUKAC/r7+FA4q7ND93OySuenajWu0md2GjUc2Ehocyvj7x9OqXCsC/QIdul+lHCE2PpYkkwTAxiMb6fVdL1qXa82i3s45cU8LuMWe/OFJZmyZAUCT0k34oc8PLj3YkR7fR39Pl3m2M0iPjjxKybwlXbr/I5eO0GpWK/ae3wvYulvCh+pFwZT1rt64ysp9K1m+Zzm/HviVfRfufkPzmFdiyBWQy+H71wKeDUScjGD48uFsOLKBErlL8EaLN6hXoh51StTBz8fP0mxX4q6Qd4LtNOCV/VbStkJbS3IYYyj4TkEuXr8I6G3qlHUuXLvAst3LWLxrMT/v/ZlrCdfIG5iXFmVa0KhUIwJ9bd8Oz187z5RNUyiQowA7nt5B/hz5HZ5FC3g2EnY8jH6L+hF9LhqAIkFFGFx3MG+0eMOyg5ytZ7Xm1wO/0rdmX77p/o0lGW7aeWYntT6uRaJJBKD3Pb157b7XqFKoSvIlCpRyhuNXjrNk1xIW71rMmoNrSEhKIDhPMF2rdKVbtW60KNPCkvegFvBsJj4xng2HN7DxyEbe++M9Lly/wDMNnuGDDh+4PMuFaxco+I6tO8eMdd37ITUHLx5k/eH1rN6/mpkRthN+c/rlZFnfZbQq18ridMqT7Dm3h8W7FrMoahGbjm0CoHKhynSr2o1uVbtRv2T9247XWEELeDZ28fpFQt4P4Ur8FQA2Pr6RxqUbu2z/3+74lkcWPkKZfGU4+NxBl+03PYwxbDmxhe2nt/P8iufJ6Z+TFf1WUKNous8lU+qu1hxcQ8uZLZMf1ytRz1a0q3WjWuFqt52zYDW9K302lj9HfqKG//+wvSZfNGHkzyOJPhudfKTbWdYdWscjCx8B4P327zt1X5khItQLrsdjtR9jZf+VXI67TM2Pa9JudjvWHVpndTzlxlbts12vpEuVLhx67hBhQ8N49b5XqV6kerYq3qnRFng2cyrmFK//9jqfbfkMg8Hfx588gXloULIBtYrWomLBigyuO9hhb7Ae83uwMGoh83vMv+2O2tnVwYsHmfzHZL7c+iUx8TEMvHcgfWr0oX3F9lZHU25i+6ntTP5zMjtO7yDseBi/DviVluVapr2ihbQLxc0cuXSEFftWEHUmiq2ntrLj9A5Ox54GbGdEXh1zNcsHU5JMEr7jfQHYPmy7W3VLXI67zLAfhzFn+xwAPu/8OY/XedziVCo7u/nHf/pf0/H18aVc/nL8q+G/GFZ/mNXR0qQF3AMcuHCA8h+UT37cqVIn6gfXp2LBioQGh1KlcKqXokmWZJLov7h/cvEDCB8aTt0SdR2e2dn2nd9H7U9qExMfk+1PklKuZ4xhzcE1TNk0hWW7lwEwpO4Q3m79drY7DyM1WsA9yNQ/pzJ/53w2Htl42/xvun9D35p9U133/LXzjFo1is///hyAEfVH8N82/yV3QG6n5XW2udvn0ndRXwJ9AxnfcjwvNXnJbfowlXMkJCWwct9KJqyfwLrD6yiaqyhD6g7hyXpPUjpfaavjZZgWcA8UEx/D1RtXWXNwDe9ufJew42GUyVeGVf1XEZcYR4ncJW47CebwpcOUmVIm+XHiG4mWD49ylMjTkby46kV+3vszc7rPoU/NPlZHUhaZtnkaI34aAUDJPCV5pdkrPFH3Cbe4kFxKtIB7uBuJN6jxcQ12n9t92/ye1XvSpUoXGpZqyIsrX2Rp9FLG3T+O0U1He9x1RowxFHqnED2q92DGQzOsjqMs8NOen+g4pyMAi3otomOljh7xPtcC7iX2X9jP6NWjk+/JeafSeUtzeKTn3oChwzcd2H1uN/uevfu1KpRnup5wnRdWvMD0sOlULVyV5X2XU65AOatjOUxKBdzaC3AohytfoDwLei4AbLd5mhkxkyJBRTgde5oKBSvQvVp3ixM61+nY0+y/sJ9q06oR+XSkx3QRqbszxrB8z3Je+eUVtp/ezguNX+Dt1m+75IbC2YG2wJVH2XF6BzU/rgnAsw2eZWqHqRYnUs5yJvYM/Rb3Y+W+lZTLX44PO3xIp8qdrI7lFNqForxGTHwMef+bF4Php0d/4oGKD1gdSTnI3yf+5sPNH7Jq/yqOXT4GwNut3+aFxi949IXOtAtFeY3cAbnZ88weKn5YkQfnPEj86/HaleLGzl09x4zwGYz5dQwAQf5BdKnShWqFq9G1aldqFqtpcULraAFXHqlCwQo0D2nOusPrOHTxkEcd0PIGxhjWHlrLjC0zWLhzIXGJcQCMbjqal5u97JRrbrsjLeDKY41tMZY2s9tQ63+1+KLzF25xrRdvdyb2DLMiZvHplk+JPhdNvsB8DKk7hCH1hlCrWC2r42U7WsCVx2pdvjVLH1lKl3ld6PVdL+aauTxS4xGrY6k7XL1xle+jv+frbV+zYt8KEpISaFK6CV81+4qe9/QkyD/I6ojZlhZw5dE6V+nMhsc30PSLpry97m1qFq3JPUXvsTqW10tISuDXA7/yzfZvWBS1iJj4GErlLcXzjZ6n/7393erCalbSAq48XuNSjelatStLdi2hxsc1uPTyJfIG5rU6ltcxxhB+Ipxvtn3D3B1zORV7inyB+eh9T2/61erHfWXu04PNGaQFXHk8EWF+j/kEvGU7uSNPQB6LE3mfJbuW8PLql4k+F02AbwCdKnXi0ZqP0qlyJ7e+RonV9M+d8gr+vv48We9JADYf22xxGu8RfTaaXgt60e3bbgT6BTLjwRmcfOEki3ov4uHqD2vxziI9kUd5jXNXz1FvRj3iE+PZ++xePTjmZJGnI2n3dTti4mMYUncI41uO1995Juk9MZXXKxRUiNFNR3Mi5gSvrH6Fy3GXrY7kcS7HXeaTsE+o/2l9anxcg2s3rvHLgF+Y1G6SFm8n0AKuvMqgOoNoU74NH2z+gOZfNmf/hf1WR/IIu87u4vGlj1PivRI89eNTxCfGM6ntJHYO30lo8D8ajspBtAtFeZ0kk8SkjZN4efXLGAzl8pfjiTpPMKb5GL2TTwYdvHiQsWvG8vW2r8nhl4NHaz7K4LqDqR9cX3+XDpTpa6GISGlgFlAcSAJmGGOmikht4H9ADiABeNoYo0eHVLbnIz6MajqKR2s+yvzI+Szfu5zXfnuN89fOM6ndJC086RCXEMekjZN4a91bAIxsNJLRTUdTJFcRi5N5lzRb4CJSAihhjNkiInmAcKArMAV43xjzk4h0BEYZY+5PbVvaAlfZkTGGngt6sjBqIV92+ZLHaj9mdaRsbf3h9Qz+fjDR56LpUb0Hk9tNdsv7TLqTTB/ENMacMMZssU9fAaKAkoABbp4NkQ847ri4SrmOiLCg5wLqB9dnxPIR9FrQiwMXDlgdK9tJSEpgzC9juO/L+4hPjGd53+Us6LlAi7eFMnQij4iUBeoAm4DngBUiMgnbH4ImKawzFBgKEBISkoWoSjmPiDDn4Tk8tewpFuxcgL+vP990/8bqWNnG1RtX6f1db5btXsYTdZ7g/fbvkydQT4iyWrpHoYhIbmAh8Jwx5jIwDBhpjCkNjAQ+v9t6xpgZxphQY0xokSLaP6ayr4oFK7L80eUUDirMnO1z+OPIH1ZHyhau3rhKm1lt+HH3j0zvOJ3POn+mxTubSFcBFxF/bMX7G2PMIvvsgcDN6QVAA8fHU8q1AnwDmN9jPgDvbnzX4jTZw4ebPuSPo38wr8c8htUfZnUcdYs0C7jYDsl/DkQZYybfsug40MI+3QrY4/h4Srley3ItaVyqMYt3LabWx7U4d/Wc1ZEskZiUyNjfxjJ2zVg6VOxAr3t6WR1J3SE9LfCmQH+glYhstf90BIYA74lIBPA29n5upTzBzYOa209vp8yUMqw/vN7qSC738uqXGb92PN2rdeerrl9ZHUfdhZ7Io1Qqlu1exojlIzh06RAtyrSgXYV2tC3fltDgUI8eLz47YjYDlgxgeP3hfNTxI6vjeD29K71SmXTiygk6z+tM2PHb37u7hu+iSuEqFqVynvDj4TT9oilNSjdhRb8VHn23d3ehF7NSKpNK5CnBX0P+Ys8ze3j9vteT5zf8rCFnYs9YmMzxbiTeYNDSQRQOKsyCngu0eGdzWsCVSqeKBSsyvuV4zFhDt6rduBR3iV8O/GJ1LId6d+O7bD+9nemdplMoqJDVcVQatIArlQlty7cFYML6CRYncZz9F/Yz/vfx9Kzek85VOlsdR6WDFnClMmFY/WFUKVSFkHyec3bxK7+8gq+PL1MemGJ1FJVOWsCVyoTV+1cTfS6ayoUqWx3FIXad3cX8yPm80PgFgvMEWx1HpZMWcKUy4aVVLwG2Qu4Jd/aZFTELX/Hl6fpPWx1FZYAWcKUyYWbXmQBEnIrgXz//y+I0WZNkkpi9bTbtK7aneO7iVsdRGaAFXKlMqFWsFseeP0atYrX4bud3xMTHWB0p0/48+idHLx+lf63+VkdRGaQFXKlMCs4TTJ3idYiJj+FG4g2r42RaxMkIAJqHNLc4icooLeBKZcHaQ2sByB2Q2+IkmXfmqu1kpKK5ilqcRGWUFnClsiBvoO2mVNtObbM4Seadv3aePAF59KxLN6QFXKksGNFgBAChn4by94m/LU6TOTevh+TK6yIpx9ACrlQWDK47mP+2/i8AdWfUZeW+lRYnyrhKhSpxJf4KJ2NOWh1FZZAWcKWyaFTTUcx9eC4A7b9uz8KdCy1OlDHVClcDIPJMpMVJVEZpAVcqi3zEh0dqPMKiXouoVLASTy9/2q1as/WC6+EjPskHZJX70AKulIN0q9aNr7t/zenY0/x7zb+tjpNu+XPkp2HJhvyw+wero6gM0gKulAPVD65Py7It+ST8Ew5fOmx1nHTrW7MvW09uZfOxzVZHURmgBVwpBxIRJre33ft7zvY5FqdJvwH3DiBPQB4+3Pyh1VFUBmgBV8rB7i12L+0rtGfKn1NIMklWx0mXvIF5GVR7EN/u+JZjl49ZHUelkxZwpRxMROhUqROnYk8xbNkwYuNjrY6ULs81eo5Ek8j0v6ZbHUWlkxZwpZyge7XutKvQjhlbZvDY0sesjpMu5QqUo32F9szaNovEpESr46h00AKulBOUzFuS5X2XA/Ddzu+YtHESi6MWE3UmivjEeIvTpeyx2o9x9PJRfj3wq9VRVDr4WR1AKU/l6+NL92rdWRS1KPkGEACFchbioSoPcen6JYbXH07r8q0tTHm7zlU6kz9HfmZGzKRthbZWx1FpEFde/yA0NNSEhYW5bH9KWS0hKYHw4+HkDczLbwd/Y/jy4YBt7PX1hOtcT7gOwOHnDlM6X2kroyYbtHQQS3ct5cxLZ/D18bU6jgJEJNwYE/qP+VrAlXItYwwiwt7ze6n0YaXk+X1q9OGv43/h7+PP7G6zqVOiDoIgIi7NN2/HPPos7MOfT/xJw1INXbpvdXcpFXDtA1fKxW4W5IoFKxL/WnzynXDm7pjL3vN7iTobReinofiO98VnvA+HLh5yab425dsgCCv2rXDpflXGaQFXykL+vv7M6jaLoyOPEjU8ilMvnqJJ6Sa3Pafs1LLIOGFWxCziEuKcnqlwUGFqFqvJxiMbnb4vlTVpFnARKS0iv4lIlIhEisi/bln2jIhE2+e/49yoSnmuknlLUrVwVYrmKsqGxzdgxhrMWMP2YdupW6IuAAOXDCTHf3Kw6egmDlw4wPZT2zl/7TwJSQnJfekAl+MuZ/na3vcUuYetJ7eSkJSQpe0o50rPKJQE4AVjzBYRyQOEi8gqoBjQBahljIkTEb0fk1IOVqNoDcKHhrN8z3I6zekEQKPPG2V4O8F5gtn59E7y5ciXruffX/Z+5u6Yy4bDG2hRtkWG96dcI80WuDHmhDFmi336ChAFlASGAROMMXH2ZaedGVQpb9axUkfMWMP+Z/cz7v5xgG0ky00PVHyAdhXapXhfy+NXjpN/Yn5OxZxKcR/GGC7HXWb7qe1En40GwM9HRxpnZxkahSIiZYG1QA37v0uBB4DrwIvGmL/uss5QYChASEhIvUOHXHtARilvdfH6Rf448gfnrp2j/+L+ty0zY22f+3NXz9F3Ud+73klIELY+tZVaxWq5JK9KWZaHEYpIbuB34D/GmEUisgP4FfgXUB/4FihvUtmgDiNUyhrGGHzGpz1m4T+t/sOXW7/ksXsfY1CdQQTnCXZBOpWWlAp4ur4fiYg/sBD4xhizyD77KLDIXrA3i0gSUBg446DMSikHERHMWMPZq2cpPqk4ieb2a51cHH0xuX98TPMxVkRUmZCeUSgCfA5EGWMm37JoCdDK/pzKQABw1gkZlVIOUjioMAlvJLB7xG4K5CgAwJzuc9J9cFNlL+lpgTcF+gPbRWSrfd4Y4AvgC3tXSjwwMLXuE6VU9lGpUCXOjz5vdQyVRWkWcGPMeiClc3n7OTaOUkqp9NIzMZVSyk1pAVdKKTelBVwppdyUFnCllHJTWsCVUspNaQFXSik3pQVcKaXclEtvqSYiZwArrmZVGPc8S9Rdc4P7Ztfcrueu2V2Zu4wxpsidM11awK0iImF3uxBMdueuucF9s2tu13PX7Nkht3ahKKWUm9ICrpRSbspbCvgMqwNkkrvmBvfNrrldz12zW57bK/rAlVLKE3lLC1wppTyOFnCllHJTHlXARaSniESKSJKIhN6x7BUR2Ssi0SLS/pb5ASIyQ0R2i8guEXnY9ckzl/2W5d/bb6zhchnNLSJBIvKj/XcdKSIT3CG3fX49EdluX/aB/W5VlhKRe0XkD3uuH0Qkr32+v4jMtM+PEpFXrM56q5Ry25fVsi+LtC/PYWXWO6WW3b48RERiRORFp4cxxnjMD1ANqAKsAUJvmV8diAACgXLAPsDXvmwc8JZ92gco7C7Z7cu7A3OAHe6QGwgCWtqfEwCsAzpk99z2ZZuBxthucPKTFbnv8jr+AlrYpx8H3rRP9wXm2aeDgINAWavzpiO3H7ANuNf+uNCt7/fs8JNS9luWLwQWAC86O4tHtcCNMVHGmOi7LOqC7c0cZ4w5AOwFGtiXPQ78175+kjHGkjPCMpNdRHIDzwNvuS7p7TKa2xhz1Rjzm33deGALUMp1iW0ymltESgB5jTF/GNundBbQ1XWJU1QFWGufXgXc/AZpgFwi4gfkxHbbw8uuj5eilHK3A7YZYyIAjDHnjLnjDszWSyk7ItIV2A9EuiKIRxXwVJQEjtzy+ChQUkTy2x+/KSJbRGSBiBRzebrU3TW7ffpN4D3gqqtDpUNquQGw//4fAn5xXaw0pZS7pH36zvlW2wF0tk/3BErbp78DYoETwGFgkjEmO90EM6XclQEjIivsn8lRlqRL3V2zi0guYDS2b/UukZ6bGmcrIrIaKH6XRa8aY5amtNpd5hlsr78UsMEY87yIPA9MwnYTZ4dzZHYRqQ1UNMaMFJGyDop49wCO/Z3f3KYfMBf4wBizP+sp7xLAsblTfT3OlNrrwPYN8gMReQP4HltLG2zf0hKBYKAAsE5EVjvrd303mcztBzQD6mNrmPwiIuHGGJf+kc9k9nHA+8aYGFcdHnG7Am6MaZOJ1Y7y/3/hwVa0jwPnsL1JFtvnLwCeyFLAVDg4e2OgnogcxPb/WFRE1hhj7s9qzjs5OPdNM4A9xpgpWYiWKgfnPsrtXT13vh6nScfraAcgIpWBTvZ5fYGfjTE3gNMisgEIxfb13iUymfso8PvNrkwRWQ7UxcXf0jKZvSHQQ0TeAfIDSSJy3RjzkbNyeksXyvfAIyISKCLlgErAZntf5g/A/fbntQZ2WhMxRSll/9gYE2yMKYutxbLbGcU7C+6aG0BE3gLyAc9ZFy9FKf2+TwBXRKSRffTJACClVrzLiEhR+78+wGvA/+yLDgOtxCYX0AjYZU3Kf0ol9wqgln20kh/Qgmz2mUwpuzGmuTGmrP0zOQV425nFG/tOPeYH6IbtL3gccApYccuyV7GNKIjmltEDQBlsByS2YfsrH+Iu2W9ZXhbrRqFkKDe2lqsBooCt9p/B2T23fX4otv7PfcBH2M9ktvIH+Bew2/4z4WYmIDe2b5SR2ArgS1ZnTU9u+7J+9tw7gHeszpqR7Lc859+4YBSKnkqvlFJuylu6UJRSyuNoAVdKKTelBVwppdyUFnCllHJTWsCVUspNaQFXSik3pQVcKaXc1P8BErSTzORZJb0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1346039478640355 = TX std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1366228835002443 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for TX\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  7.7019\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fe98b798-0b94-4a44-807f-4bd15057e377",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5283093020704941 0.5290184773398103 0.5347060037543987 stateGOP, calc w and without elPaso included\n"
     ]
    }
   ],
   "source": [
    "stateGOP38 = 0.\n",
    "for t in range(nTracts):\n",
    "    stateGOP38 += HDweight[t]*HDvGOP[t]*37/38+0.\n",
    "stateGOP38 += 1./38 * 0.31858  #adjusting statewide GOP by adding in elPaso R-D lean\n",
    "print(stateGOP, stateGOP38,stateGOP2,\"stateGOP, calc w and without elPaso included\")\n",
    "stateGOP2 = stateGOP38  #adding back in the panhandle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba8396d4-437e-4466-b34c-85a293d20059",
   "metadata": {},
   "outputs": [],
   "source": [
    "# STOP HERE 3/24/22 - NEED TO GO BACK AND FULLY REINCORPORATE EL PASO INTO RESULTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b7eb02f8-1189-41ed-b7c7-efe7c5890da6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5283093 0.5283441954978533 true stateGOP, calc w aelPaso re-included\n"
     ]
    }
   ],
   "source": [
    "#3/24/22 - I have reincorporated.  Let's check\n",
    "stateGOPredone = 0.\n",
    "stateGOP = 0.5283093  #np.sum(vtdTrump)/(np.sum(vtdTrump)+np.sum(vtdBiden) is an alternate if I read precinct data back in\n",
    "for t in range(nTracts):\n",
    "    stateGOPredone += HDweight[t]*HDvGOP[t]\n",
    "print(stateGOP, stateGOPredone,\"true stateGOP, calc w elPaso re-included\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8f4571d7-8c42-4c95-8c9b-d8e33fffece8",
   "metadata": {},
   "outputs": [],
   "source": [
    "stateGOP2 = stateGOPredone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by 3e-05 0.52834 HD avg vs true 0.52831\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAARiElEQVR4nO3dbYxc113H8e+vm0aUtuCKLMWyHewi09aqSBuWxFCo+sCD7VRYSH2RFBrVqmRZtasiQDT0BRJCSO4b1EQEWyYNxaJgVX0AQ01NJRQKap16U9KkjglaTKi3McqG0pQ2EsHNnxczEaPdWe+1Pd49O/v9SCPPvefcnf8cr/XzuffOmVQVkiS15kUrXYAkScMYUJKkJhlQkqQmGVCSpCYZUJKkJl230gUMc8MNN9TmzZtXugxJ0jJ46KGHnq6qyfn7mwyozZs3Mz09vdJlSJKWQZJ/H7bfU3ySpCYZUJKkJhlQkqQmGVCSpCYZUJKkJnUKqCQ7kjyeZCbJXUPak+SefvsjSW4eaFuX5BNJ/jnJ2SQ/Oco3IEkaT0sGVJIJ4F5gJ7ANuCPJtnnddgJb+4+9wKGBtruBz1bVa4CbgLMjqFuSNOa6zKBuAWaq6lxVPQccA3bP67MbOFo9p4B1SdYn+T7gTcBHAKrquar65ujKlySNqy4BtQE4P7A929/Xpc+rgDngj5P8U5L7krx02Isk2ZtkOsn03Nxc5zcgSRpPXQIqQ/bN/5bDxfpcB9wMHKqqNwDfARZcwwKoqiNVNVVVU5OTC1a8kCStMV2WOpoFNg1sbwSe7NingNmqerC//xMsElDSWrf5rs8s2vbEwduWsRKpDV1mUKeBrUm2JLkeuB04Pq/PceDO/t1824FnqupCVf0HcD7Jq/v93gY8NqriJUnja8kZVFVdTHIAOAlMAPdX1Zkk+/rth4ETwC5gBngW2DPwI94HfKwfbufmtUmSNFSn1cyr6gS9EBrcd3jgeQH7Fzn2YWDqykuUJK1FriQhSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWpSp4BKsiPJ40lmktw1pD1J7um3P5Lk5oG2J5I8muThJNOjLF6SNL6uW6pDkgngXuDngFngdJLjVfXYQLedwNb+41bgUP/PF7ylqp4eWdWSpLHXZQZ1CzBTVeeq6jngGLB7Xp/dwNHqOQWsS7J+xLVKktaQLgG1ATg/sD3b39e1TwF/m+ShJHsXe5Eke5NMJ5mem5vrUJYkaZx1CagM2VeX0eeNVXUzvdOA+5O8adiLVNWRqpqqqqnJyckOZUmSxlmXgJoFNg1sbwSe7Nqnql748yng0/ROGUqSdEldAuo0sDXJliTXA7cDx+f1OQ7c2b+bbzvwTFVdSPLSJC8HSPJS4OeBr46wfknSmFryLr6qupjkAHASmADur6ozSfb12w8DJ4BdwAzwLLCnf/grgU8neeG1/qyqPjvydyFJGjtLBhRAVZ2gF0KD+w4PPC9g/5DjzgE3XWWNkqQ1yJUkJElN6jSDkiS1Y/Ndn1m07YmDty1jJdeWMyhJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTXCxWWgXWyuKg0iBnUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJnX6wsIkO4C7gQngvqo6OK89/fZdwLPAu6vqywPtE8A08PWqevuIapc6W+wL//yyP6ldS86g+uFyL7AT2AbckWTbvG47ga39x17g0Lz29wNnr7paSdKa0WUGdQswU1XnAJIcA3YDjw302Q0craoCTiVZl2R9VV1IshG4Dfg94NdGW74kvw5e46rLNagNwPmB7dn+vq59Pgz8JvD8pV4kyd4k00mm5+bmOpQlSRpnXQIqQ/ZVlz5J3g48VVUPLfUiVXWkqqaqampycrJDWZKkcdYloGaBTQPbG4EnO/Z5I/CLSZ4AjgFvTfKnV1ytJGnN6HIN6jSwNckW4OvA7cA75/U5DhzoX5+6FXimqi4Av9V/kOTNwG9U1a+MpnRJS/H6lFazJQOqqi4mOQCcpHeb+f1VdSbJvn77YeAEvVvMZ+jdZr7n2pUsSVoLOn0OqqpO0AuhwX2HB54XsH+Jn/EA8MBlVyhJWpNcSUKS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUpE5r8UnSC1whXcvFGZQkqUkGlCSpSQaUJKlJXoOSpDGy2DXC1Xh90BmUJKlJBpQkqUme4pPULG9pX9sMKEljx2AbD57ikyQ1yRmUpGUxTneXaXk4g5IkNcmAkiQ1yVN8klbUpW5oWO7X83RjW5xBSZKaZEBJkprkKT5pjVruU2vS5TKgJI3McoaeATv+PMUnSWqSASVJalKngEqyI8njSWaS3DWkPUnu6bc/kuTm/v7vSfKlJF9JcibJ74z6DUiSxtOSAZVkArgX2AlsA+5Ism1et53A1v5jL3Cov/9/gLdW1U3A64EdSbaPpnRJ0jjrMoO6BZipqnNV9RxwDNg9r89u4Gj1nALWJVnf3/52v8+L+48aVfGSpPHVJaA2AOcHtmf7+zr1STKR5GHgKeBzVfXgsBdJsjfJdJLpubm5juVLksZVl9vMM2Tf/FnQon2q6rvA65OsAz6d5HVV9dUFnauOAEcApqamnGVJWtO8jb7bDGoW2DSwvRF48nL7VNU3gQeAHZdbpCRp7ekygzoNbE2yBfg6cDvwznl9jgMHkhwDbgWeqaoLSSaB/62qbyZ5CfCzwIdGV74kLY8rXWTWxWmv3JIBVVUXkxwATgITwP1VdSbJvn77YeAEsAuYAZ4F9vQPXw/8Sf9OwBcBH6+qvx7925AkjZtOSx1V1Ql6ITS47/DA8wL2DznuEeANV1mjJGkNciUJSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKT/Mp3Sepz/bu2OIOSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJ1cylZeRq2ePJv9drwxmUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUnexSeNmHd0SaPhDEqS1CQDSpLUJANKktSkTtegkuwA7gYmgPuq6uC89vTbdwHPAu+uqi8n2QQcBX4IeB44UlV3j7B+aUV4nUm69pacQSWZAO4FdgLbgDuSbJvXbSewtf/YCxzq778I/HpVvRbYDuwfcqwkSQt0mUHdAsxU1TmAJMeA3cBjA312A0erqoBTSdYlWV9VF4ALAFX130nOAhvmHStJa5Iz8Uvrcg1qA3B+YHu2v++y+iTZDLwBeHDYiyTZm2Q6yfTc3FyHsiRJ46xLQGXIvrqcPkleBnwS+NWq+tawF6mqI1U1VVVTk5OTHcqSJI2zLgE1C2wa2N4IPNm1T5IX0wunj1XVp668VEnSWtIloE4DW5NsSXI9cDtwfF6f48Cd6dkOPFNVF/p3930EOFtVvz/SyiVJY23JmySq6mKSA8BJereZ319VZ5Ls67cfBk7Qu8V8ht5t5nv6h78ReBfwaJKH+/s+WFUnRvouJEljp9PnoPqBcmLevsMDzwvYP+S4f2T49SlJki7JlSQkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTer0QV1pLfKrEKSVZUBpTTOEpHZ5ik+S1CRnUBobzoak8eIMSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJD8HpeZc6vNMTxy8bRkrkbSSDCitiCv9UK0fxpXWDk/xSZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKa5OegJGkNWI0fgHcGJUlqkgElSWpSp1N8SXYAdwMTwH1VdXBee/rtu4BngXdX1Zf7bfcDbweeqqrXjbB2Nc5liSRdjSVnUEkmgHuBncA24I4k2+Z12wls7T/2AocG2j4K7BhFsZKktaPLKb5bgJmqOldVzwHHgN3z+uwGjlbPKWBdkvUAVfV54BujLFqSNP66nOLbAJwf2J4Fbu3QZwNwoWshSfbSm31x4403dj1MknSVWr3Dr8sMKkP21RX0uaSqOlJVU1U1NTk5eTmHSpLGUJeAmgU2DWxvBJ68gj6SJHXWJaBOA1uTbElyPXA7cHxen+PAnenZDjxTVZ1P70mSNN+SAVVVF4EDwEngLPDxqjqTZF+Sff1uJ4BzwAzwR8B7Xzg+yZ8DXwRenWQ2yXtG/B4kSWOo0+egquoEvRAa3Hd44HkB+xc59o6rKVCStDa5koQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUmdljqSFuPXuku6VpxBSZKaZEBJkppkQEmSmuQ1KEnSoi51nfmJg7dd09d2BiVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkh/U1ZJcEFbSSnAGJUlqkgElSWqSASVJapIBJUlqkjdJCPBGCEntcQYlSWqSASVJalKngEqyI8njSWaS3DWkPUnu6bc/kuTmrsdKkjTMktegkkwA9wI/B8wCp5Mcr6rHBrrtBLb2H7cCh4BbOx6rZeJ1JkmrSZcZ1C3ATFWdq6rngGPA7nl9dgNHq+cUsC7J+o7HSpK0QJe7+DYA5we2Z+nNkpbqs6HjsQAk2Qvs7W9+O8njHWq7lBuAp6/yZ+j/OZ6j5XiOnmM6WkuOZz40stf64WE7uwRUhuyrjn26HNvbWXUEONKhnk6STFfV1Kh+3lrneI6W4zl6julotTCeXQJqFtg0sL0ReLJjn+s7HCtJ0gJdrkGdBrYm2ZLkeuB24Pi8PseBO/t3820HnqmqCx2PlSRpgSVnUFV1MckB4CQwAdxfVWeS7Ou3HwZOALuAGeBZYM+ljr0m72ShkZ0uFOB4jprjOXqO6Wit+HimauglIUmSVpQrSUiSmmRASZKatOoD6mqWYdJCHcbzl/vj+EiSLyS5aSXqXC26LvWV5CeSfDfJO5azvtWmy3gmeXOSh5OcSfL3y13jatLh3/v3J/mrJF/pj+eeZS2wqlbtg96NF/8KvIreLe1fAbbN67ML+Bt6n8naDjy40nW3+ug4nj8FvKL/fKfjeXXjOdDv7+jdbPSOla671UfH3891wGPAjf3tH1zpult9dBzPDwIf6j+fBL4BXL9cNa72GdTVLMOkhZYcz6r6QlX9V3/zFL3Ptmm4rkt9vQ/4JPDUcha3CnUZz3cCn6qqrwFUlWO6uC7jWcDLkwR4Gb2AurhcBa72gFpsiaXL7aOeyx2r99CbnWq4JcczyQbgl4DDy1jXatXl9/NHgVckeSDJQ0nuXLbqVp8u4/kHwGvpLbDwKPD+qnp+ecpb/d+oezXLMGmhzmOV5C30Auqnr2lFq1uX8fww8IGq+m7vP6m6hC7jeR3w48DbgJcAX0xyqqr+5VoXtwp1Gc9fAB4G3gr8CPC5JP9QVd+6xrUBqz+grmYZJi3UaayS/BhwH7Czqv5zmWpbjbqM5xRwrB9ONwC7klysqr9YlgpXl67/3p+uqu8A30nyeeAmwIBaqMt47gEOVu8i1EySfwNeA3xpOQpc7af4rmYZJi205HgmuRH4FPAu/1e6pCXHs6q2VNXmqtoMfAJ4r+G0qC7/3v8S+Jkk1yX5XnrfnnB2metcLbqM59fozUZJ8krg1cC55SpwVc+g6iqWYdJCHcfzt4EfAP6w/7/+i+UK0kN1HE911GU8q+psks8CjwDPA/dV1VdXrup2dfz9/F3go0kepXdK8ANVtWxfaeJSR5KkJq32U3ySpDFlQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkpr0f5YZo/lBroqKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.25284, should have been 0.24754\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for FL\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 5.325\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for FL\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 4.701 GA seats out of 14\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAA5WklEQVR4nO3dd3hUVf7H8feZyaQSWggQaoDQe9UgIL0JIqKuomJBQRdcFMuqLIrYV1F+FlR0LSi7CiqI0pEqhBJAINJLIPRe0pPJ+f1xM2mkZzJ3Jvm+nmeezNxpnwvJd86ce865SmuNEEIIz2MxO4AQQojikQIuhBAeSgq4EEJ4KCngQgjhoaSACyGEh/Jy5ZtVq1ZNh4aGuvIthRDC423duvW81jo453aXFvDQ0FAiIyNd+ZZCCOHxlFJHc9suXShCCOGhpIALIYSHkgIuhBAeSgq4EEJ4KCngQgjhoQos4EopX6XUZqXUDqXUX0qpV9K3T1FKnVBK/Zl+GVz6cYUQQjgUZhhhEtBbax2rlLIBfyilFqff977W+t3SiyeEECIvBRZwbaw3G5t+05Z+kTVoRak7F3eO2ORY7NqOPc3ukp8ACoVSCquy0q9RP5pVa2byv4QQuSvURB6llBXYCoQBH2utNymlBgHjlVKjgEjgaa31pVyeOwYYA1CvXr1ihVx0YBH/XPFPvK3eAORcw1zn+DzJen9+9znu11qb9jM/ObMXhkJlv61Urvfltb04z8m63VnPOX71OO4i9oVYArwDzI4hxHUKVcC11nagnVKqMjBPKdUK+AR4FaM1/iowDXg4l+fOBGYCdOrUqVgt96izUUSdjaJL7S5UD6gO5F90ct6f332O+x2trmL9LMlzc2TLKWfW/BT2g6ywH3CFeU5+H6Ylfc75hPMMChtEgC0Aq8WKVVlL/acjT5W3qwDQK7SXFG/htoo0lV5rfVkptRoYmLXvWyn1OfCbk7NluKnuTQA8f9PzDG8+vLTeRpRzWmtm7ZjFgv0LMrZ9c9s3JiYSIn8FFnClVDCQkl68/YC+wNtKqRCt9an0hw0HokorZOfanangXYGlh5ZKARdOp7Vm0YFF3DH3DhJTEzO2e1u9sVqsJiYTIn+FaYGHAN+k94NbgDla69+UUt8qpdphdKFEA2NLK6S31Zu+Dfuy+OBitNYFdjsIUZAUewrrY9YzZfUU1hxdk7F9YNhA/t7p7wwIG5BxzEUId1WYUSg7gfa5bL+/VBLlYWCjgczfO5895/fQIriFK99alDFaa7xfu744n5h4glqBtUxIJETxeMxMzEGNBwGw+MDiAh4pRN4uJ17GMjXz1/6b277h9NOn0S9rKd7C43hMAa9XqR4tgluw+KAUcFF0e87tQb2iMkaXAMy5Yw6j2o6iRoUaJiYTovg8poADDAobxLpj64hNji34wUKk23t+Ly1mZHa7dQjpwMEnDnJnyztNTCVEyXlcAU+2J7PqyCqzowgP8tKqlwCYcvMUkv6VxNYxW2lUtZHJqYQoOY8q4N3qdSPAFsCiA4vMjiI8xJKDS5i7ey4AN9a5UUaWiDLFowq4j5cP/Rv159f9v5Km08yOI9zcxuMbGTTbOPh9f5v76d+ov8mJhHAujyrgAMObDefEtRNEnpSTI4v8rTi8AoBnuz7LrOGzZP6AKHM8roAPaTIEL4sX8/bMMzuKcHMdQjoA8M6GdxgxZwQrj6w0OZEQzuVxBbyKXxV6hvbk570/F7ianyjfBjcezI93/kjXul35dd+v9JnVh/7f9mflkZVcSbxidjwhSszjCjgY3Sj7L+xnz/k9ZkcRbm5EixGsf3g9Jyae4OWbXybyZCR9ZvWh+rvVeXbZs9IIEB5NufIXuFOnTjoysuR91yeunqDO+3V4rddrTOoxyQnJRHkRmxzLqiOruPX7WwFjnZ36leqTbE/matJVejfozbfDv8XP5mdyUiEyKaW2aq075dzukS3w2hVrc0PtG5i3V/rBRdFU8K7A0KZDSZiUwBdDv+D+NsaSPufjz3Mp8RI/7fmJjjM7SstceASPLOBgdKNsPbWVY1eOmR1FeCBfL19GdxjNF7d+wf4n9hP7YiyrHjAmiO05vwfLVAvPLX9OCrlwa55bwNPXBZ+/d765QUSZ0TO0J6mTUxnb0VgZ+Z0N79D207Z88+c3pKalmpxOiOt5bAFvEtSEFsEtpBtFOJXVYuXTIZ8S/2I8o9uPZtfZXTz4y4OM/GkkRy8fNTueENl45EFMh8krJ/PGH29w+unTBAcEO+11hXBYdGARt/z3lozbT97wJN3rd6dtjbZsOrGJq0lXSUxNJOZKDNUDqhOfEk+twFrc0/oeKvpUNDG5KEvyOojp0QV8+6ntdJjZgf/c+h8ebn/d+ZSFcIqElATm7Z3H91Hfs/jg4ly7U2wWGylpKRm3w6qG8Vqv1/hbq7+5Mqooo8pkAdda0/CDhrQMbslvI0vtnMpCZEi2J7Px+EY2n9hMi+AWNA1qil3bCasaxtWkq1TwrsCCfQsYMWcEAEvuXcKAsAEmpxaerkwWcICJSyfy8ZaPOf/seQJ9Ap362kIU16WES9ScVpOeoT1Zet9Ss+MID1emxoFnNbzZcJLtybLErHArVfyq8PxNz7Ps0DK2ntxqdhxRRnl8Ae9atyvVA6rLaBThdsZ3GY+vly/P//68LH8sSoXHF3CrxcqwpsNYeGAhiamJZscRIkNwQDBjO45lxeEVNPmwCTO2zJCJQcKpPL6AA9ze/HZik2P5/fDvZkcRIpt3+r3D/0b8j+CAYMYtGsf0jdPNjiTKkDJRwHs36E1Fn4r8vOdns6MIkY3NauPuVnez4eENhNcJZ8qaKZyJPWN2LFFGlIkC7m315pbGt7Bg/wLsaXaz4whxHaUUk7pP4mrSVRr8XwM+2PSBdKeIEisTBRzg1qa3cj7+PBuPbzQ7ihC5uqXJLUQ9HkUVvypMWDIh45RvQhRXmSngA8MGYlVWftsvE3qE+2pZvSVRj0cBsCp6lclphKcrMwW8sm9lbqp3E4sPLjY7ihD5quJXhUo+lVgdvdrsKMLDlZkCDnBL41vYcWYHJ66eMDuKEPm6p9U9RByPYNeZXWZHER6sTBXwwY0HA8isTOH2ngp/Cquy8sTiJ2StcVFsZaqAtwxuSd2KdVl0UAq4cG9Ngprw2ZDPWHN0DbXfq81dc+9i7dG1ZscSHqZMFXClFIMbD2bF4RUkpSaZHUeIfD3c/mF+vedX+jbsy6roVfSZ1Ydf9v5idizhQcpUAQejGyU2OZY/jv1hdhSPEBMTw7p164iJiTE7SrmjlGJIkyHMvn02+8fvp2VwS2774TbG/jrW7GjCQ3iZHcDZ+jTog7fVm0UHFtGnYR+z47ilmJgYduzYwfHjxzlzJnNWYI0aNahSpQpaa5RSBAQEEBISQnx8PP7+/sTHxxMaGkrdunVNTF82VfGrwqZHNhH6f6HM3DaT+pXr89xNz+FlKXN/osKJClwPXCnlC6wFfDAK/o9a65ez3P8M8A4QrLU+n99rlcZ64LkZ8N0Ajl05xp5xe0r9vdxJTEwM69ev59q1azRo0AAfHx/8/f05deoUsbGxAFy5coXTp08X6XWvXg3k0qXKHDtWHz+/RIKCbmLs2MqEh5fGXpRv209t58mlT7L26Fpub347Hw76kFqBtcyOJUyW13rghfl4TwJ6a61jlVI24A+l1GKt9UalVF2gH3DMyXlLZHDYYJ5c+iSHLx2mYZWGZsdxmogIWL0aevY0bq9cGUPNmjsICIglISGBY8cy/xtOnjxZ7Pc5dqwOR4+GEhoaTZ06x7HbLXz77f3Y7Va0VgDMmgVt28KNN8KoUUgxd5L2Ie1Z8+Aa3ot4j6eXPU3kyUiiJ0SjlDI7mnBDBRZwbTTRY9Nv2tIvjmb7+8BzgFsdeRnc2Cjgiw4sYnyX8WbHKRFH0b58Gd57D9LSwGaDKlXOMHr0LGJisg9BK+nfeUxMHWbNGoXdbsVqtfPAA7M4f75qevG2YPzXK7SGP/80Ll9+aWSUIu48E8MnYlEWnlr6FC/+/iJv9HlDiri4TqEOYiqlrEqpP4GzwHKt9Sal1K3ACa31jgKeO0YpFamUijx37lzJExdC46DGhFUN89jx4BER8Oab8M9/ws03w4svwr//DampRgFPSgIfn3isVnu2gp31emHWSdLaeD2tMy/R0aEZxdput3D4cCiVK1/BKNy5v2hyMjz/vJE5IqJEuy6y+HvnvwPw1vq3WHlkpclphDsq1BESrbUdaKeUqgzMU0q1ASYB/Qvx3JnATDD6wIsftWgGhw1m5raZxKfE42/zd9XbltjMmTBuHNjt+Rfh+Hj/9EJrBxQnTtQiOjoUf/94EhL8CQ2Npm7d4wW+39atHbhypRI+Pol07bqRevWisVjs2O0KrS1s2dKZ7dvbo7U139dZu9a4+PnB7+nLsju6e6RlXjzeVm+iHo+i96zePDD/ASbcMIFkezIjWoygWbVmZscTbqDIJzVWSr0MpAFPAPHpm+sAJ4EuWus8j5C56iAmwLJDyxjw3QAWjlyYMUPTnUVEGK3sBQuMVnFh1KkTQ1DQec6eDebUKceBLgVovLzs3HDDJk6frkn37mupX98YJqi10VLXWmG3W/nmm/s5frweoKlTJ4aqVS+yc2cbijvCVCkYNgyWLjVa5t7eRkGXIl58ETER3DH3Dk5eM45rKBT/vOmfvN7ndSyqzI0EFrko9kFMpVQwkKK1vqyU8gP6Am9rratneUw00KmgUSiu1KN+D/xt/izc7/4FPCICevUyukaK4vjxuhw/nnNInwYspKbC+vU3UadODLVrn0rvIrGwePEAAgOvkpLiQ3R0aPrzjX7t48frpRfz4tMafv01s3smOVn6x0sqvG44x586zpWkKySlJjFp5STeWv8WG45v4PXer9OtXjezIwqTFKYLJQT4RillxWiWzdFau/2arb5evvRp0IdFBxdljGt2V6tXF714581ogRs/ITT0KCdPhrBrV2vS0iA11cb69TeRnOyb4znOk5YGXl5Ga9zbO3PUjCg+pRSVfSsD8PnQzwmvE86klZPo/lV3+jTow/gu4+lSuwsBtgAq+VYyN6xwmcKMQtkJtC/gMaHOCuRMgxsP5tf9v7L3/F6aBzc3O06egoKc/YoKmy2RlBQf9u1rQkREOHa7ow87s7g7u3A7aA1PPQWVK0sfeGlQSjG6w2juaX0PM7bMYPrG6Qz/YbhxH8ZyEhPDJ9Kjfg+ZCFTGlen/3ayrE7pbAXcMDwwKgv/8x/mvn5LiC2jOnXP0dGUt1qX/beTqVXj77VJ/m3LN3+bPM12f4ckbn+T3w78TfTma6MvR/Gf7f+gzqw+B3oHc1+Y+xncZT4vgFmbHFaWgyAcxS8KVBzEdWn/SmuoB1fl9lPucsT4iAvr0MbpNCnvA0tN06QLTp0vr2wwJKQn8su8XFh1YxJy/5pBsT+aRDo/waq9XqVGhhtnxRDHkdRCzzB/CHhw2mHVH13E16arZUTI4+rzLavEG2LLF+JCSceGu52fz4+5WdzNr+CyOTzzOY50e4/NtnxP+n3CizkaZHU84Udkv4I0Hk5KW4lYnkA0KKnvFOzQ0+22tITHRmHIvzFPNvxozbpnBxBsncjr2NG0+acPQ/w1l68mtZkcTTlDmC3jXul2p6FPRrWZlbt9udgLnstnAsQyLJctvlNbw1VfSCncH0wZMI+apGCb3mMzKIyvp/11/zsW5Zma0KD1lvoDbrDb6N+rPogPGcEKzzZwJn39udgrnUSpzij9Ap05w222Z0/pTU40uI2G+IP8gXun1CqsfWM3FhIt8uf1LsyOJEirzBRyMfvBTsafYcSbfZVtKXUQEjB9vTJMvKxxrqDhs22b89PYGq1XGgbujzrU7E1Y1jClrpshp3DxcuSjggxoPAsw/2fHq1WWreOcmNRV++cUo6o8+KtPo3dUvd/9C3Yp16TurL/P2zDM7jiimclHAa1aoSceQjqYX8J49jRmKZZ3WmR9Uq1dLH7g7ahHcgs2PbqZupbo8ufRJs+OIYioXBRyM0SgRxyO4EH/BtAzh4fDww5n9wxaLcck5y1+p7AcDPY1SxuWrr2DyZBlO6K4q+1ZmTIcxHLtyjHt/vpfFBxYTmxxb8BOF2/DgMlE0gxsPJk2nsezQMlNzjBoFvr5G/7CPD3zyCbz+Onz2GfTvbxRuR5+yWcu3KAU9ehT/+Y6FrFJSjJa4Y0Er4X7+ccM/eP6m55m/dz6D/zuY5h83Z+LSiQz57xBeX/s6CSkJZkcU+Sg3Bbxzrc5U86/GooPmdqOEhxv9wq++avxs3drY3ro1TJliFHVHcW/b1pyMjjHc3t7Ffw3HqBQ5kOne/Gx+vNn3Tc48c4Z5f5tHNf9qvL/xffZd2Me/Vv2Lqv+uSq9vevF/G/+P8/Fus9ioSFfmp9Jndf+8+1lycAmnnz6N1ZL/CQpcwTGlPuu62ZB5CrVp08w76KmU0V+fmlq4s/vkxmKBMWPknJmeJk2nYVEW1h5dy/y981l+eDlRZ6OoV6keHw36iCFNhrj16p5lUbmdSp/V4LDBnI8/T+RJ8z5Eslq92ijeWbsZwsONmZrvvmvuiBWtS1a8HerVk+LtaRwniehRvwfvDXiPXY/vYu2Da0lNtnLr97dyw8cDuJhw0eSUAspZAe/fqD8WZWHhgYVmRwGMbgXHeGmr1ZjN6DilmrtOtVfq+mnzWbVrZ8zMtFiMbiDpOikbvE525+Kre1ArX2fL2VX0nDnULSbGlXflYFBbpiD/IG6scyOLDixiaq+pZsfJ6A+fNcsYsfH550bhy6/lbbFkFnfHSJXSKva5/X1aLNCkCURHZ26zWo0MNhvMmGFsk/Nhli2rV0NKgg967YuQ4seuARMJ+zCMrnW7Uje1JxwYwtBeNeT/28XKVQscjG6Urae2cjo2z1N3ulR4uNHNkJpqFO6cxTtnV6OjWCtlTFt3NbsdlmUZyKMUdOwIY8dmdgGFh8MLLxj3y5nqy4Zscxg2jce69ENqe7Vm0d7lvLn7Ed6MDaPXfZvk/9rFyl8BTz/Jw5KDS0xOksnRleJoXWdt+eb1LdViMYYjmt3VojVERsI332Tf7jhAK+PAy4bwcHjoofQGRZoNNo9n0JX5PK1PoZa/Az6xJN1ynwwXdbFyV8Db1WxHSIUQ02dlZuXoSunbt/DPsdth3brivZ+zJgk5vh1kPXlxRITR6p416/oDtMKzZZ3D4BgaWrHtanS/Z40HBB2kSzeZCORK5aoPHIzzCQ5uPJgfd/9Iij0Fm9VmdiTAKOIjRmTvniioj7u4x5CaNoV9+0rWes864chiMf6gg4Iyh0VarZlfuWUceNngaGhkPb7x55a9Gfd3CepPr27+puUrj8pdCxyMbpQrSVfYELPB7CjZXLiQWbSVMsZQf/KJcXDQMT09Nzm3WyxGAc3L0KHGCJHiDOW1WDILs9bG7b59jT/sCxcyW912u/GV2zFhKa+DW44Wu3SxeAbH8Q3H/+eBiwcy7pv/wNcZQxCFa5TLf+2+DfviZfFyq24UMFo1jpmYvr7GV9YxY2DNGmO6/dix13d/WCzQuXP29VX69jVGg/j5ZQ5RzPr4ypWNojp2bP6FPq8PjYkTs88YnTLF2H7smFHcHV+xR43K/seek/STe772NdtnXK/1Xi3e/kPOZO1K5bKAV/SpSPd63U2fVp9Tzmn2jsLnaPWMGmUUTEcRd4y1Hj06+/oqU6YYhf/3340lXbMWfZstszujXj24++7sGXr0yByb7usLI0dmv1/rzA8AR04wCvDnnxdtGdncJjIJz3Jvm3t5r/97Gbef//151CuK5h835/uo701MVj6Uuz5wh1sa38Izy5/h2JVj1KtUz+w4GRzD8PK6z9EHGRRkdFk4+iJbt75+7HV4uLEt69DDhx4yrjv6qh2tbEd3yMCB8NZbma+1enXm/WAUdsd7ON7nzTczCzEUfvalY/SNYykB6Sf3PBZl4anwp+jVoBeP/fYYm05sAmDv+b3c89M9dKvXjToV65icsgzTWrvs0rFjR+0udp/drZmC/nTLp2ZHKVUbNmjt56e11Wr83LBB6zfeMG6D1haL1jZb9vtze77jcZ99Vrj3KEq+N94o2nOE+0qxp+hf9/2qmULG5VLCJbNjeTwgUudSU8ttC7xZtWaEVg5l0cFFjO001uw4pSa3kQOQveU7fXr21nxhnl/Ux+SXT2bvlR1eFi+GNBmSbduJqyeo7FvZnEBlXLlajTCncQvH8fWOr7nw3AV8vXzNjuNSEREy3V2Unp92/8Qdc+8AoFZgLR7v9Djz986ncVBjvhv+nVusBupJZDXCXAxuPJj4lPhyeWLXnMPBhHCWiAjY/8sI1vZNYe6dc4lPiWfyqslsPbWV76O+56VVL5kdscwo1wW8V4Ne+Hr5ut1wQiE8VdahoQP6eVH7yh2cnHiSyEczv3mvj1lvYsKypVwXcH+bP71Ce0kBF8JJchsa6mfzo2OtjrzW6zUA9pzfw7JDy2Q5Wico1wUcjG6UAxcPcODCgYIfLITIV9Y17nMODZ3UYxKzb5+Nr5cvA74bQI+ve/DR5o/YdWYXadpNF8B3c+X6ICbA4UuHafRBI6YPmM6EGyeYHUcIj1fQAfKk1CS+2PYF0yKmceTyEQCq+Fbh5tCbubXJrdi1nWbVmtGtXjeX5nZneR3ELPcFHKDZR82oX7k+S+9banYUIcqVI5eOsO7YOtYdXceig4s4ee1ktvt3/303zYObm5TOfeRVwAscB66U8gXWAj7pj/9Ra/2yUupVYBiQBpwFHtRan8z7ldzX4MaD+XjLx8QlxxHgHWB2HOEGZJilazSo0oAGVRowqu0oUtNS2Xd+HxtiNjDmtzEAtJjRAoCvh33NXS3vws/mZ2Zct1OYPvAkoLfWui3QDhiolLoReEdr3UZr3Q74DfDYsUG3NL6FZHsyK4+sNDuKcAOyyJY5vCxetKzekkc7PkrK5BRe7PZixn0P/vIg/m/40//b/sQmy5rjDgUW8PSZnI5/MVv6RWutr2Z5WADgsYeUu9XrRgXvCjIaRQCyyJY78LJ48Xqf10mdnMq6h9bx8s0vA7D88HIC3wwsl3M3clOoUShKKatS6k+MrpLlWutN6dtfV0rFAPeSRwtcKTVGKRWplIo8d+6ck2I7l4+XD30b9mXhgYUytEnkO5JCuJbVYqVbvW5M6TmFM8+cydj+1NKnTEzlPgpVwLXW9vSukjpAF6VUq/Ttk7TWdYHZwPg8njtTa91Ja90pODjYSbGdb3DYYGKuxhB1NsrsKMJkeS3rK9zHc12fMzuCWyjSOHCt9WVgNTAwx13/BUY4J5I5bmlyCwALDyw0OYlwB7LUgPvx88o8gNm7QW8Tk7iPAgu4UipYKVU5/bof0BfYq5RqnOVhtwJ7c3m6x6gVWIuOIR35bf9vZkcRotzL7VR7gT6BTLjBmKtR/d3qHLx40KR07qMwLfAQYJVSaiewBaMP/DfgLaVUVPr2/oDHz4IZ0mQIEccjOB9/3uwoZZKc/1IURn6jgKYPnM5tzW4DoPGHjdlzbo85Id1EYUah7NRat08fMthKaz01ffuI9NtttNZDtdYnSj9u6RrSZAhpOo031r1x3YQCUTIyNE8UVkGjgD4e/HHG9fvm3UdqWqpL87mTcr8WSlbta7YnpEII7298n9rv1Wbrya1mRyozZGieKKyCRgHVCqzF5kc2A7Dt1DZu+vImlh9a7vKc7kAKeBZWi5XNj27OuC0L7DiPDM0ThVWYUUCda3dmeLPhAGw+sZn+3/UnMTXRxUnNV25PqZaX2oG1qRVYi5PXTrLjzA5aVW8l03edoCSnXRPlT2FOtffz334GQL2iAIhNji13Z9aSFngOSilW3L+CFsEtePTXR/F/w5/Dlw6bHatMkKF5orjyOgB+JfFKxvVNxze5OJX5pIDnonlwc2YMnpFx+/jV4yamEaJ8y+8AeEpaSsb1If8bksuzyzYp4HloH9Ke/o36A/DA/AdMTiNE+ZK1xZ3fAfBq/tWY3GOyWTFNJ33geajoU5HvR3xP1X9XLXf9akKYydHiTk42DnhPn278dNzOeQD81bWvmhHTLUgLPB9V/KrQvFpzoi9H89X2r8yOI0S5kLPFfeFC/qNSutfrnnH9XJx7LphXWqSAF+C/I/5LYmoiDy94mBWHV5gdR4gyL7chp/kdAF/7UObSsg/+8qCrYroFKeAFaFujbcYiOo8vfNzkNEKUfUVdDTLrKLGLCRdLOZ17kQJeAKUUXw77EoCDFw+SbE82OZEQZV/OFnd+6+ik2DNHorzW6zUXJXQPUsAL4e5WdzP3zrkATFg8gaTUJJMTCVF+FLSOTtNqTVly7xLqVqxL32/7ol5RtP+sfbmYSS0FvJDuaHEHD7V7iE+3fkrnzzuz/th6syMJUS4UZh2dAWED2DNuD2M6GCdD/vP0n1inWun+VXfafNIG9Yrii21fuDS3KyhXnkKsU6dOOjIy0mXv52xaa+bunsujvz7K1aSr9GvYj0X3LsLLIqMxhSgtOYcVFtQvfuTSEebunsuRS0fYdXYX62MyG1uRj0bSsVZHF6R2LqXUVq11p+u2SwEvupgrMfSe1ZuDFw/SMaQjWx7dglLK7Fj5ckyIkHVIhCcqye9vij2F4T8Mz3a2rYk3TmTagGlOzViapIA7mdaa2364jQX7FvDd8O+4t829ZkfKU1FbMEJ4msIU+MOXDvP1n19nTPxZcu8SBoQNcFnGksirgEsfeDEppXirz1sAxKXEmZwmf7IWt/AExT1jU2FPFtKwSkOm9prKpkc20bhqYwbOHkjQv4NYE72m5OFNIgW8BBxnAnlt7WtM2+C+X8dkLW7h7kpyxqaiNFAiIuD3WV34vMMuwBg33u/bfiXKbiY5+lYCrWu0ZuaQmYz5bQzPLH8Gn0sduLazl9v1M8ta3MLd5VaEC/t76mig5LVWikPWrkSbtw3+aWw/MdFzzwYpBbyEutbtmnH9mTF1SD3jnv3MhVkgXwhXytpvXdginJvCNFAiImDKFEhKgrQ0INlCL8tkVqW9ysifRzL/b/MJ8A4o+U65mBTwEhq/eHzG9ZTz9UgrRgtCiPImtwPrpfUt0fFejuJtsRjv+XrfqXx38QIzImcw8ueR/HL3L857UxeRPvAS2HlmJ6ujVwNwb+hz+Fh9pJ9ZiELIq8ukOGdsyq//PGfL22KBvn0zvyFP7TUVgAX7FqBeUXyw6QOPmsEpLfASGLdoHAB/7/R3/t3vJcY1kX5mIQqjJF0mOeV1EHPWLPjqK0hJySzePj5GQXf8fQb5B3HoH4do9EEjACYsmUBqWioTwycWP5ALSQu8BHqF9kKhmBE5g0pvVeLJv27gWpcXadqufK2IJkRRFXXFwfz07GmMsFLK+BkUZLTEP/ss75Z3Vg2rNOTok0cZ1XYUAG/98Vbxw7iYTOQpoSuJV9gQs4F1x9ax7tg6NsRsoJJPJZ7t+ixP3PAEFbwrmB2x2GT2pvAEERHQq1dma/6hh+Dzz40WORiF3de34A+Kjcc3Ev4f4wH2l+xYlPu0b/OayCNdKCVUybcSgxoPYlDjQYDRlzbs+2G8uPJFXlz5Ilefv0qgT6DJKYtOZm8KT7F6NaSmgtbGT8jsnrFa4eGHYdSo/H9/d57ZmVG8q/hWcavinR8p4E6QptM4E3sGm9XGK2teydg+uv1otzqfZlFa1CUZlyuEK+XsTx81Ctq3h59+ghEjYMyYgl/j3p+NpTCC/YM5++zZ0g3sRFLASygxNZHOn3cm6mxUtu27/76b5sHNTUp1vaK2qJ15kEmI0pRzHDjAk08av7vr1kHr1tl/13NryLQIbkHU2SjOxZ9j6cGlHrNGimd8T3BDqWmpzPlrDnfMuSOjeLcIbsHMITOJnhDtVsUbir4eijMPMglR2rIOQcz6u56UZIw6iYgwLo8/bvSX5xxyOPv22UzrbyyHMXD2QL7+82uzdqVIpAVeDLvP7eauuXfx17m/qB1Ym/va3MeARgO4q+VdeFu9zY6Xq+K0qGX2pvBEjt91xwiUFStgzRqjjzwlxfgJ2bsGvSxejOs8jqeXPQ3AyiMrebDdg2btQqFJAS+CpNQknlv+HJ9v+5xAn0Dm3jmX4c2GY7VYzY5WIFkPRZQXjt/1KVOM4p2WZhRryCzeSmVvyEREwMpVNryUjVSdQrNqzcyIXmRSwItg34V9fLD5AyzKwqGxhwgJDDE7UpFIi1qUF+HhRgFft84o3l5eRvG2268fmZJ5fMiC5cZp0O8fzN87nzY12jCkyZCM10y2J3Pq2inqVqrrNqNUpIAXQevqralbsS6XEi955NBAIcqT3A5u5vYNdNYsSEw0CryOGkG1fh+x5eQWhv5vKI2rNubWprdS2bcyM7bM4FTsKdrUaMPCkQupU7GO63cqB5nIU0TV36nOufhz7Bm3xyO+ZslkHCGu5/i7CAqCJ57I7GLx8YGVKzWN257ni21fsPjgYjad2ESyPZlW1VsxstVIXl37Kkn2JJoGNeWRDo/wjxv+UernxS32KdWUUr7AWsAHo8X+o9b6ZaXUO8BQIBk4BDyktb6c32t5egHXWmOZauHOFncy5845ZscpkEzGEeJ6Wf8ulDK6VbQ2ro8dC598kv3xWmviUuIIsAWglGLXmV3M3T2XtUfXsuboGtrWaMtdLe/C3+ZPXHIcscmxWC1WOtXqRNe6XakeUL3EmUsyEzMJ6K21jlVK2YA/lFKLgeXAC1rrVKXU28ALZCyRXjZpjA+7ubvnorV2+xMZy2QcIa6X9e9CqcwDm1obE4ByUkplWxKjdY3WtK7RGq018/fO58mlTzJp5aSM+20WG2k6Dbs2puO/2utVnu/2fKn0mxdYwLXRRI91ZEu/aK31siwP2wjc4fR0buan3T8B4GP1MTlJ4chkHCGul/XvImsL3GKBCxcK/zpKKYY3H85tzW4j2Z5MXEoc/jZ/fL18SUhJYNupbXy4+UMmrZzExuMbmXPnHKfPzC5Ux41SygpsBcKAj7XWm3I85GHghzyeOwYYA1CvXr3iJzVZXHIcD/3yEF4WL849e87tW98gQweFyE3Wv4ugoMxZm8Vt5Cil8PHywccrs2HnZ/Pjpno30bVuVxJSE1iwbwEHLx6kVfVWztoNoJAFXGttB9oppSoD85RSrbTWUenhJwGpwOw8njsTmAlGH7gzQpth6P+GEpcSR+danT1qBIoMHRTieln/Llq3Lp1GjtaapYeWsvPMTupXqk+L4BbOe/F0RTp0qrW+rJRaDQwEopRSDwBDgD7alcNZXEhrzW/7f2NV9CoAlty3xOREQghncnYjx55mZ9GBRby/8X1WRa+iTsU6zL1zrjl94EqpYCAlvXj7AX2Bt5VSAzEOWt6stY53ejI3cDr2NINnD2b76e0E2AJYOHIhVf2qmh1LCOGmriZdZeB3A4k4HkHtwNpM6z+N8V3Gl9oSG4VpgYcA36T3g1uAOVrr35RSBzGGFi5P7w/eqLV+rFRSmmT4D8PZfno7ABsf2ej0/ishRNlxOvY0I+aMYMvJLXwx9AtGtR2FzWor1fcszCiUncB1g2u01mGlkshNXE68zOYTm/H18uXok0edMpZTCFE2LT24lFHzR3Et6Rrfj/ieES1GuOR93WNCvxtaHb2aNJ1G7wa9pXgLIfI0ZfUUBs4eSLB/MFse3eKy4g2yFkqeetTvARjLSgohRFb7L+xny4ktzNk9hwX7FjAobBA/3fUTfjY/l+aQAp4HL4sXCkWToCZmRxFCuJGos1G0/6w9qWmpBPkFMbXnVF7o/kKpr4eSGyngeYi5EoNGs/PMTpJSk7IN0hdClE8L9i1g1LxRBHoHMvv22fRv1N/U8wFIH3geWlZvyaTuxvoG62PWm5xGCGG2vef38thvj2G1WNn0yCYGNR5k+slcpIDn47429wHGAU0hRPmz59weun3ZjRYft6DljJYk2ZOY97d5NA5qbHY0QLpQ8nXgwgHAOJWaEKL8OXjxYMY38EndJzG+y3hqVqhpcqpMUsDzcUOdGwivE86/N/yb1LRUngp/yi3OwiGEcI0jl48A8PNdPzO8+XCT01xPulDyUT2gOqseWMVjHR/jvY3vUff9upyNO3vd4yIi4M03jZ9CiLLh0MVDPLX0KW6ufzPDmg0zO06upIAXwMfLh0+GfEJYVWPiab9v+7H73O6M+x1n95g82fgpRVyIsuHQpUOk6TQe7/S425zEOCf3TOWGoh6P4qUeL7HzzE5e+P2FjO25nfVGCOH5lh9ajkLRtFpTs6PkSQp4Ifl4+fDSzS8BEFIhJGO74+weVquc9UaIskJrzexdsxnefDjtarYzO06epIAXgUVZqFmhJj/89QOnY08DmWf3ePVVOWmwEGXFnvN7OBV7ikFhg8yOki8p4EWglOKFbi9wOfEys3dmnoAoPBxeeEGKtxBlgdaaOX/NAaBbvW4mp8mfFPAi+mbHNwA0D25uchIhRGn4Pup7XlnzCj3q93D7tZBkHHgR/LznZ7ad2sYTXZ5gcOPBZscRQjjRtlPb+HnPz0yLmEabGm1YOWql244+cZACXgRv/vEmtQJr8W7/d3O9PyJCzgAvhCf6/fDv9P+uP2k6jeHNhvPJLZ+Yvs5JYUgBL4Kjl48C5Hp+O8d48ORkYzSKHNAUwjMcvXyUW7+/laZBTVly3xLqVapndqRCc+/vB25kQ8wGzsWfI8A7INf7ZTy4EJ5nx+kdDJw9EHuanR/v+tGjijdIC7zQ1kSvAWDunXNzvd8xHtzRApfx4EK4v9vn3M7hS4f5YOAHtAhuYXacIpMWeCFcSrjE1LVTCasaRseQjrk+RsaDC+F5Zt8+Gy+LFyuOrDA7SrFIC7wQLiRcIDE1kdbVW6OUyvNx4eFSuIXwJE2DmhLkF+T2o03y4pmpXaxRlUbYLDY2Ht9odhQhhBO9vPplzsSd4Z83/dPsKMUiLfAC2NPsLD+8nDSd5vbTaoUQRePr5YtVWbmxzo1mRykWaYEXYPrG6QyaPYhAn0D+ccM/zI4jhHCiAFsAdm3HnmY3O0qxSAEvgONs9F8N+4q2NduanEYI4Sxaa6LORRHoHYhGmx2nWKSAF2B0+9HUCKjB6+teNzuKEMKJJq2cxI+7f2TCDRPwsnhmb7IU8AJoNPEp8dgsNrOjCCGc5K0/3uLNP95kbMexTO011ew4xeaZHzsudCb2DNeSr3Ho0iHsaXaPWB9BCJG7hJQEPon8hBd+f4GRrUcy45YZ+Q4NdnfSAi9AgyoN6N2gN2fjzhL2YRjz9swzO5IQogiWH1pOh886UH96fQLeCODpZU8zpMkQvh72tceO/3bw7PQu8vNdP/PpLZ8SYAtgxJwR/Lb/N7MjCSEKac3RNWw/vZ0gvyBeuvkl5v9tPvP+Ng+b1fO7RaWAF0Il30qM7TSWiNERNK3WlKH/G8qExRPQ2jOPXAtRntzX5j4A7ml1D1N6TmFYs2Eee9AyJyngRRDoE8i2Mdt4pP0jfLD5A5YdWmZ2JCFEPi4mXOTDTR8CEBIYUsCjPU+BBVwp5auU2qyU2qGU+ksp9Ur69jvTb6cppTqVflT34GfzY2qvqSgUzy5/lksJl8yOJITI4bPIz2j9SWtqvFuDGZEzGNd5HPe2vtfsWE5XmBZ4EtBba90WaAcMVErdCEQBtwNrSy+eewoJDGHunXPZdXYXn0R+YnYcIUQWp2NP88TiJzgff55nuz7Ljsd28NHgjzx6tEleCizg2hCbftOWftFa6z1a632lms6NjWgxgrY12jJ943RS7ClmxxFCpPPz8iMkMIS45DjOxZ0jwJb7SVjKgkL1gSulrEqpP4GzwHKt9aZSTeUhutbtyoWEC6SmpZodRQiRrpJvJVbcv4LhzYcze9dsOs7sSMyVGLNjlYpCFXCttV1r3Q6oA3RRSrUq7BsopcYopSKVUpHnzp0rZkz3dOLaCdJ0Gv5v+DN/73yz4wgh0jUOasw3t33Dt8O/5UrSFTbEbDA7Uqko0igUrfVlYDUwsAjPmam17qS17hQcHFy0dG7ug4Ef0CGkAwCrjqwyOY0QwiH6cjTPr3iehxc8TDX/avSo38PsSKWiMKNQgpVSldOv+wF9gb2lnMsj1K9cny9v/RKLsvDRlo/YcXqH2ZGEEMD4ReN5e/3b9GvYj42jN5bJIYRQuBZ4CLBKKbUT2ILRB/6bUmq4Uuo4EA4sVEotLc2g7qptzbZEPR5FSIUQun7Zlc8iP5MJPkKYKCk1iT+O/cG9re/lx7t+pFHVRmZHKjUFTkfSWu8E2ueyfR4gC4MAzYObs+XRLdR6rxaPLXyMDiEd6Fy7s9mxhCiXft3/K1eSrjCy9Uizo5Q6mYnpJCGBIUzuMRmAr//82twwQpRjH2z6gIZVGtKvYT+zo5Q6KeBO1KV2FwCOXjlqchIhSt8PUT/Q5MMmqFdUxiXo30Ecu3LMtExrotew7tg6Hmj7QJlYrKogZWNFFzfh5+UHQPNqzU1OIoTzaa3ZfGIzCakJdKvXjbt/uvu6x1xMuEj96fV5sN2DtK7emuoB1anoU5GaFWrSqVanUl2+9fjV49zz0z00qNyAZ7o+U2rv406UKw+4derUSUdGRrrs/cxw89c3s/XkVg5POEz1gOpmxxHCaaZvnM5TS58CwN/mT3xKPDfVvYk/Hv4DgDSdxv92/Y9xi8ZxJelKrq/RIaQDo9uPZmiTodStVNdp2bTWtPm0DVFno9j1+C5aVS/0VBWPoJTaqrW+bs0pKeBOtmDfAoZ9P4waATV4f8D73N3q7jK5BoMoX7af2k6HmR0ybj/a4VH8bf683vt1AryzT1XXWpOSlsKyQ8vw9fLFx+pDj6+vH4c9uv1oagfWZvPJzSw5uITW1Vuz5dEteFu9i/w3cyb2DDWn1QSgZXBL3h/wPv0alZ0+cCngLqK15pllz/DLvl84dOkQI1uP5KthX+Ft9TY7mhDFpl7JLKirHlhFz9CeRXp+sj2ZxQcW06xaM7ac3MLY38YSnxKf73P6NOjDIx0eoXHVxlxLvsa+8/s4G3eWiwkX6VK7C7c3vx0fL5+Mx0edjWLF4RV8vOVjDl48yKZHNmUcl/J0UsBdLE2n8ca6N5i8ajI+Vh/8bH74evled/Gx+uS6Pb/7fLyK9hw5j6coKUcBP/30aWpUqFHi19NaY9d2UtNSWXt0LYcuHmL2rtmsj1lf4HN9vXxJTE3EoiwE+wejlOJ07OnrHjfhhglMHzi9xFndQV4FXA5ilhKLsvCvHv+iZXBLIo5HkJiaeN0lyZ5EYmoilxIvZd+empTtMSXlZfHKVtz9bf742fyMn15+193ObVtet3Pe5+vl6/HnGRTZpem0jOtV/ao65TWVUngpL7wsXvRv1B8aweOdHwcgPiWe1dGrCasaxtm4s2w+sZmYKzG0CG7BA+0ewMvixeIDi9l0YhNnYs8AoNEcuXyE7vW6o1BoNCOaj3BKVncmLXA3l6bTSLYn51rcc/swuG57jscnpCYYl5QE4lPiSUhN/5nltuO6XduLldnXyzf3D4acHwBF+GDI6/Xkw6L0Hb96nLrvGwcc9csyy9gM0gL3UBZlyegKcbUUe0qeRd5xO7/7st1O/2A4E3vmuseU5MPC0T1V6A+EQn4w5Pb88toVFeQXlHH9xNUT1K5Y28Q0Iisp4CJPNquNStZKVKJSqb9Xij2leB8MWR+b4zHn4s/l+u0iJa14J+DwtnoX/ptCIT8Y8no9d/qw8LP58XT400yLmEad9+uQMjmlzJwU2NNJF4ood1LTUov2wZDbY1Nz73bK+ZzifljYLDb8bf4EeAcQYAvIdj3jZ/p1f5t/9u3e+d/nb/MvVtdTlbercDnxMiBdKa4mXShCpPOyeBHoE0igT2Cpv5fjw6Kg4w65fVOIS4kjPiWeuJQ44pLjiEuJ41ryNU7Hns62LS45Dk3xCurtzW/np7t+KtRjLz53EctUo/DHJcddN/5buJ4UcCFKkSs+LLTWJNmTshV0x8+cHwBxyXGcjTvLuxHvAtC+5nULjeb9Plk+JGRymnuQAi6Eh1NKZRzoDiKowMefizvHx1s+RqMZ13lcod/n/Yj3M6772/yLlVU4l4zBEqKcCQ4IZs6dc0ixp9Dus3bsPLOzUM8b1mwYQLlYZ9tTSAEXohwa0mQI6x9ez7Erx5i6ZmqhnvPFti8A2HJiS2lGE0UgBVyIciqsahgAkScLHhmWkJLA2+vfBmDxvYtLNZcoPCngQpRTf537CyjcAcl+3xor+4XXCS/T55j0NFLAhSinHCtkZl3rJC+ORaZmDp1ZqplE0cgoFCHKqRvr3AjAsSvHqPRWJU49fYpT106x9dRWDl48SMMqDRnSZAg+1swlW521mJVwDingQpRj5589T7V3qnE16SoBbxQ8MaeCdwUXpBKFJQVciHIsyD+ItJfSWLBvAV/++SU96/ekbqW6JKYmEn05GpvFRuSpSJpUbcLYTmOp6FPR7MgiCyngQpRzSimGNRuWMc5beA45iCmEEB5KCrgQQngoKeBCCOGhpIALIYSHkgIuhBAeSgq4EEJ4KCngQgjhoaSACyGEh3LpSY2VUueAoy57Q9eoBpw3O4RJyvO+Q/nef9l316qvtQ7OudGlBbwsUkpF5na26PKgPO87lO/9l313j32XLhQhhPBQUsCFEMJDSQEvufK8wn153nco3/sv++4GpA9cCCE8lLTAhRDCQ0kBF0IIDyUFvJiUUu2UUhuVUn8qpSKVUl3St4cqpRLSt/+plPrU7KzOlte+Z7m/nlIqVin1jFkZS0s+/+9dsvyf71BKDTc7q7Pls+/9lFJblVK70n/2Njurs+Wz70FKqVXpv+8fuTyY1louxbgAy4BB6dcHA6vTr4cCUWbnM2Pfs9z/EzAXeMbsrC78f/cHvNKvhwBnHbfLyiWffW8P1Eq/3go4YXZWF+57ANANeAz4yNW55JRqxacBxwkCKwEnTcziannuu1LqNuAwEOf6WC6R675rreOzPMY3/XFlTV77vj3LY/4CfJVSPlrrJBfnK0157Xsc8IdSKsyMUDIKpZiUUs2BpYDC6IrqqrU+qpQKxfgl3g9cBf6ltV5nWtBSkM++BwArgH7AM0Cs1vpd85I6X177nn7fDcCXQH3gfq31PNOCloL89j3LY+4AHtNa9zUhYqkpaN+VUg8CnbTW412ZS1rg+VBKrQBq5nLXJKAP8JTW+iel1F3Af4C+wCmgntb6glKqIzBfKdVSa33VZcGdoJj7/grwvtY6VinlurBOVsx9R2u9CWiZ/sf+jVJqsdY60VW5naG4+57+3JbA20B/V2R1tpLsu1mkBV5MSqkrQGWttVZGtbqita6Yy+NWY/QFR7o6Y2nJa9+VUuuAuukPqwykAS9prV1/cKeUFOH/fRXwbHn4f0+/rw6wEnhIa73ezJyloaD/d7Na4DIKpfhOAjenX+8NHABQSgUrpazp1xsCjTH6hMuSXPdda91dax2qtQ4FpgNvlKXinS6v//cGSimv9Ov1gaZAtBkBS1Fe+14ZWAi8UBaLd7pc991s0oVSfI8C/5f+R5sIjEnf3gOYqpRKBewY/YEXTcpYWvLa9/Igr33vBjyvlErB+Obxd611WVtuNa99Hw+EAZOVUpPTt/XXWp81IWNpyfN3XikVjXGA0zv9IH5/rfVuV4SSLhQhhPBQ0oUihBAeSgq4EEJ4KCngQgjhoaSACyGEh5ICLoQQHkoKuBBCeCgp4EII4aH+H16PKs9gDRsSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the FL map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  768212.3214285715\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAABV00lEQVR4nO29eZwc5Xng/326Z0ZoxEgadEuj0YWQYcRhSQjJxgZscMCRjUG2wRCyJMu1P5ysk02yxFkrBCc+NscPe0MWMGGdbECAkAwYQwzY3EZiNAogjYQOhObQfYykQdfMdL/7Rx3zVnV1d/XM9Bz08/18BFNd1VVvVXc/z/s+pxhjUBRFUUqXxEAPQFEURRlYVBEoiqKUOKoIFEVRShxVBIqiKCWOKgJFUZQSp2ygB1AoY8eONdOnTx/oYSiKogwpGhoaDhhjxkXtG3KKYPr06axdu3agh6EoijKkEJGmbPvUNKQoilLiFFURiMiVIrJZRLaJyF0R+/9URN5x/20QkZSInFHMMSmKoihBiqYIRCQJ3AdcBZwDfENEzrGPMcb8rTHmAmPMBcCfA68aYw4Va0yKoihKJsVcESwEthljthtjOoDHgKtzHP8NYHkRx6MoiqJEUExFMAVosbZb3dcyEJFK4EpgZRHHoyiKokRQTEUgEa9lq3D3JeDNbGYhEblNRNaKyNr9+/f32QAVRVGU4iqCVmCqtV0D7Mpy7PXkMAsZYx40xiwwxiwYNy4yDFZRFGXAaGhq476Xt9HQ1DbQQ+kRxcwjqAdmi8gMYCeOsL8hfJCIjAIuAX6niGNRlKLy6Jpmnt+wm6vmTuKGi2oHejhKP9LQ1MaND62moytNRVmCR25ZxPxp1QM9rIIomiIwxnSJyDeBXwJJ4GFjTKOI3OHuv9899BrgBWPMsWKNRRm6NDS1sXr7QRbNHNOrH1dfnSeKR9c08+2frQfg9a0HAFQZlBCrtx+koytN2kBnV5rV2w+qIrAxxjwHPBd67f7Q9k+BnxZzHMrQpK9mWsWesT2/YXfGtiqC0mHRzDFUlCXo7EpTXpZg0cwxAz2kghlyJSaU0sGeaZ3qTPPAqx9w/tTRBc/qwzO2Veta+3R1cNXcSf5KwNtW+o9irvbiMH9aNY/csmhAx9BbVBEoA062H/KimWMoSybo6EpjgBc27uWlTXupKEuwbEkdbcc7Au/JdR5vxpZMJlixtoWutOmz1YE3+y+Wj6A/Bd1AC9VCGSz2+fnTqofE88qGKgJlQMn1Q54/rZqvzq9h+ZpmP+7YWx0se3oDadMtzIGc5/FmbDsPn+Cxt5v73J57w0W1RTEH9aegGyxCtRAGi31+qCnQMCVTdG76Xb/w/ynRFBIC11fhcqu3H+RUp/ND7uh0fsg2S+fVMKw8EUhKMUBX2gR+/FECIYq5k0dRUZYgKQxKe274uca9r76gP6/VV3irvYH8PD0F+vcvbObGh1YPyRDSklgRhIX/9Lt+wY4f/PYAjabvaWhqY+W6VgS4dl5Njx2q3/jJat/htfzW7LPBvpw5VldWdM/23W0bbzZ/70tbeGPrAQxOpmIyIRhjAj/+bA67hqY2vvHgW3SkDEmBWz8zk6rh5YNu9hb1XPvTETkUnZ6DwT4/WFYlvaEkFMFQI9syM+r1hqY2rnvgN3SlnWNWNLT6Qjzq+HC8e0NTG6vWtfLKlv10uCfpcB2q2WzvK9e1cqrTsdvH/eJnu6e24x0kxDH5JMTZjjp26hmVlJclSKUcIRXlI8gmEFaua6Uj5aiblIGfvL6dJ+74VKxnW2wBY1/DFigd7nO987Iz+03QDQah2hMG2j4/FBVoGFUEgwx7VpgQqJs8isUzx9B+qosVa1voTBmSCeGeq+dyw0W1rFzX6isBcATIynWtQLfNHGDmuNOZO3kkT73jJHe/vvUAb394kGfe3UU6ovCH91J4lrpsSR1PNrT6+5MJyfvFz7WCCP+IqisrMq53z7ONdHSlKUsI1y+szbrqCQsET8geaD8VOC5tCCivqPHZz69Y9vLgZy0sOW+S/1mkDbSf6OS+l7exaOYY7rzszD69djYGWqgORYaqArUpCUWw4we/HTAPDWazkGczNzjC4N3WI7zbeiRwTFfa8D+echKYwkIOYMXaFgT82SXAtn0fsW3fR4HjPKUQxdzJo/zx2Mve5zfspivlKBcBLp0z3rclR/0AGprauPelLf49dXSlufelLXzr8rN8oWP/iKKu522n0obJo4fHWn2sXNfKkw2tdKXSJBLBslfJZFB5ZbON9/VyP7zCsK+bNoZn3u3+PAR46I0PSRtDWTLBV+fXsLSHZj+l+PRUgQ4WJ3NJKAIY3MLfxraZ5yJtcJRBxMGdKcP+9lORVf/iIDgmGsicsV81dxL1Ow75oZivbN7nh3SGZ832jNez7acNvLH1APU7DvnHh39EFWUJOjrTiAh1k0b614uz7Pau6SkeAJMKPqSvL5iaNbw0js+hUMKKyVvp7Dx8AhEB44zPGChzfR8iQiptfOW5fE0zq9a1DolIHiUegylKq2QUwWDHmxm823I49nuiTDoeBvj82RN4YePejH0TRw6j9oxK3t6RGd2QEEcA2oJv6bwajPv/+dOqmTOxKiMU0074qq6soO14BzsPn/BnvAmB2jMqaTp4PKdvYf60apYtqfPDQ3/61o5If0A2vFm292gEJ5oEY0ilHefy0nk1GdeMWtoXutzP5mcIK6YOK/w1Ia7jO22oKO/2fVRXVnDPs43++wrxx/Q1g2XWOtgp9DkNJiezKoJBQJSw6C3jq4ZRNSz6493Xfoo9RzNNSgAzx45g5rjTAcex/J2n1pMykBTHXGTP4Bua2niyoTWQ8PXixr0YHMFflkxQlhBfAN/22Vnc82xjwB/g2cDDDuS06Q4PbTvekWEjj5U8lhC+tmAq17qCP9ePNGppX8hyP9vsLkoxJRLi31/awMLpo7lkzviMsc2ZWMWqda2sWNviP8P+dkQOplnrYKYnz6lQJ3MxFbIqgkFAWFjEQcje3CGZEPa1n+Kx+pbI/blWEtv2H2Pb/mO8vHkfqZTB80OnDHzn6Q3MmVgV+BKePbGK91qPdJthrGukUmmuX1jL5NHD/VXCzYun07j7KHWTRgacwJ7Anj+tOkOY7zp8goamtpzOXXtM4RWMR6E/nvAPz4uwijp3IOKns9sPUl1ZQcI1/5QlnfusmzyKZc9sIO2arN7e0cZXPplp//cU0bXzagZsRt7bWWuprCayPadc91+Ik7nYClkVwQDT0NTGzsMn/FIKcckmyxdOr+adlsO85M7Me0pnKvPdqbQJfMFzrWISboKPNxu3j00IvPXBwW4beMrw6JpmVqxt8RXCI7cs8u3qy98O7sv1o7N/LEvn1fRYED26ppllT28glTYMc002d/+80f+MHq9vZsbY05k5dgS3XzLLKYeREDpd5fnmtgOs+fAQ6XSaVBpEHMd63eRRtB3vYPqYEQHnvVeoLmq8AxHJ442jurKix76SUlpNRM3u49x/3M+22GYkVQQDiC1MpaeeXYsvnDOB86eOZm1TW5+ZmMJUV1bw6JpmHnztg6xKIJlwzEjXXVjL/GnV3PfytsCKx1mROGGwXe7yxFYIK12n6JTRw+lKubNsa9+yJXUBh7KXhGZHXHV0OmG0q9a1+uGZXshtPhqa2lj29AZ/bB2dTvRSp6WoU+nuSKwXNu5l2hmVdLmKzbtHW7Eb45jOXti410mISwY/8KvmTsoqOPp7Vh0VMhzXR2MTzou45+eN1E0ZNSSjn/J9BlGze+973xfCu9i5CqoIBghPmJ7sdISF6aXkTiaE2y+ZxeY97b4pwnMy9iX3vrSZfe0dOY9JpeG91iNs3tvInIlV/pe4ozNNmm6H9M2Lp/OTNz4kZdmqbKeo976ww7TteEfAoXzPs851wlnKB9pPBcIz/+Jn62k+eIy7vnh2zvGv3n6QtPWBJBLCVXMn8db2g3RFrJQAmg4dz/vs7Hs0acMXzpnAic4UdZNG0na8w1da4TDW/p5Vh2efUT6aOPifu3suLxT6ybUtLL9t8aBUBrkc/vk+A3t2b6/0vSTI3gjvYucqlKwiGEjb5Q+e28T9r20v+H3TzqjMKnBSacOd/9bA/mMdpNPObHvJeZNy5gr0hHxKwMOblXu2cu9L7PkKvJwB4wpcz4mKMSSTCXYePgEQMBHZP6iV61p905ItNO0s5bFVw0iI+ELdAPe/tp3aMSOA7NVCPbu+Md3Je3MmVjkKFpPTPxOHBI7Z7PZLZgHdgr4sIRmC44FXuycL/RVZ0lezT7s8iF2muzNlinofPf1t53P4x53Z2+fJlwRZCMU0EZakIgjbf4s1y4qqAfTomuYeKQHIP+vcYyWXpdKGd1oO85ULJrNh11F2tR3neGd8H0RfkMbJYH5z2wGuX1jrh216QjsscJYtqeOVzfv41aa9PPZ2d9z80nk1vvD1zhHObq6urODlzfsQERIY30cwd/Io/uJn6wOC++E3P/Tt869vPUDzwWN+7SGAe55tpCvtCPxPzRrjz9b9RDrpzokI4yg0Z7BJV6inXMV07hQnS9y+lp1s15UynFszkrlTRnHtvBo272kPhP8mYmRx9xXXus+8twJs/rRqvnX5Waz58JBvKisPJfT15aSsN36JbAI/m/0/25jt83SlDc0FrBbt++jPiWrJKYKGpja+4yoB6K542duIkqj9dg2gx+qb+fwnJvDSpsy4/mJggB0Hj7PjYPSXcMro09h1+GTRfAk2aeMo38frWxCMP2OvmzyKmxdPDwjGZU9vwLO+eDWPvNIa5UlhqessDmc33/3MBr+ekAA3L57uz6CaDx4LKt+QHe7B1519Fa5z2zfXAa9tPcDrWw+QTOArmbKEcOmc8Wzf/xHb9nd3WPWs/kkRvnahEx10988bSblCYf3OI2ze2+6XsPjGg2/RmTIBc9b6nUfYtPsoAGtC1T+nnVGZ8V3ra4ERFqTXhnIuesL8adUsv3VRZMRVXzqUw1nsha6gsq2EvJWNN/7Ne9r9iLeoMYdNYuEEyjj30d/mwJJTBKtck4KHCAXPsuJ8UOEaQKk0kcldA8XuflICNvZzt23G37vmXN+5Zh+TECcM1hPwHSnDynWtLJ1XE2g0s/foSf8YcAT4T17fzhV1E5k/rZq7vng2tWNG+KYgwO8x7I0FHMEhZIbmGnA/S6d6qQFe2rQ3sCJIuiYp51inFEbb8Q5fYXnX8YTTOy2HA2O2j/Ec4+EAgjNGBCuzFmNl29PoFDvKKMqxnM2s0VfRMOEsds/8VshvO58dfqXrwxHwJyvZxnztvBoadzrfb89MGvfeBiLRrOQUQfin9/mzJxT8kPN9UI+uaebfN+zpg9EWj/41EuXmx7/a4juVh5U7TuVEQrjl4hm8FZoVH2g/xap1rXx29jgAXtm8j/dCtZjA+aF6JijPPOfVN/J4vL6ZDTuP+D/qZDJB3eRRTB59GjsPn4wca9pA2prFe5wxooL9H3X4x7Sf6KT9VBdlCQkI/GTSSaT79fv7cj4TQ2YAwewJVf7fUZFNcWzX+VYP4Vlx+4lObvrnNRm+FFvwb9h1hCcbWuns6g4PLlZSVTbs32RC4NNnjs34vOMQR2HZhIsuhotGekSVWM/GQFQzLaoiEJErgR8BSeAhY8wPIo65FLgXKAcOGGMuKeaYls6r4UnL1OA57ArBTxIiM9vzW4/9R587aIcihThU9xw9xY0PreaRWxYFnMpeiQWbX72/z181uKb4yOtUJB2/gdeHAODxtS087kar3HBRLW3HO1i/84g/3gtqRvkz7Gz3IeL8+MN5Fp4S8N7zkzc+JJ02GbP6jq40r2ze5zvJbZLiTExe2bLfD5u1sTPFoyKbcgkMryeD973PFrVjz4rbT3T6JjXP2TtnYlWgblKUn6SQmWxfRcPYv8mKskSPlEAU4XwKO2RagK8tmArg+6GE7mKF9kfslViPQ7EjhKIomiIQkSRwH3AF0ArUi8gzxpiN1jGjgX8CrjTGNIvI+GKNx2P+tGru/vJcHq9vZvzI0wp+/w+e28QDr233Zz7nThnFqnWtbN7TzlP/0RpZv6eUSAhcv9CZOT66pjn2+7z6+54w27DrSEa2tRAyL9GtDGymjD6NOy+bTdvxjsBsvCtluP/VD7hg6mgWzRzDopljnHIP7jFrd7T5KyVxz9MaWhkIjq3+g/3Hcio6b5xRYcEvbdobuK7H3CmjePB3F/i2bjvSBqDR9R1A0A6dEGf1lKsKrN2TwTOxZRMw3qz4pn9eE3j98fpmNu9tz1sKxUsmjDuT7W00TENTG/c820jKjZZbtqSuKP6SZUvq/NVPKuWYJfe1n+J6V8EClCXwI7+SCQGRHoWP9ncSYTFXBAuBbcaY7QAi8hhwNbDROuYGYJUxphnAGJN7vdwHNDS1WRmiR3h1877YMc3hiJ+0gfodbdSXuPC3SRtHWNa5ZazjIhDoReCdyyZK+Mwcf3pGee1dh09y988bueSscX44qcdLbj2kiqTwxXMn+XkBUSuLsBIAx+RkO4ijyLcSShuQiIMWzxzj1166au6kDEVQN2lkwLwTXj2F+ynYM8pwvmK2/EX7/OExjB95Gut3HgnMiD17vF0+oyfJZ73BLtFijIk9887HqlADprbjHX7U2oH2U07l3VAGfyoNX7+whimjh/uCfyiU2CimIpgC2MVuWoGLQsecBZSLyCtAFfAjY8y/hk8kIrcBtwHU1vauQfjq7QcDGaJxY5obmtr4/vObenXtwUJv4+CtysmR5zPEXwZ7jKqs4JXN+yLtsLnGMWPsCD48cCwjKa2jK82LG/dS5nbl9nZ7R3WkTE4TXpwhnF8zivU7j8Qer4fXZhPcFcaYSq6sm8hP39rhC/PzpmQq0qOnuvxMdC+/4c7LzszIYLUzqj3FcO28Gla4dvzyLNFAUTPgK86ZwL6jJ7nuwlrmTKzi9a37AwX9PMHvOYnDtaj6g2LY1Bua2lixtsX/HiQSwrsth/nRS1t8v0zU5+5FtvWmxtVAUExFEDXpCD+6MmA+8HlgOPCWiKw2xmwJvMmYB4EHARYsWNCrYJdFM8cEBFk4pjkKr59vIbWABjNxH6Cb35VxvAAXTq+m/WSXP/t7dv1uulJOWeWRw8oKKqcNcOhYR8FRVcYQmJElAHGrnXp0peGKcyZwsjOVMcMuhPDKAuC6C2u57kICTtt8JBPCrRfP4Kdv7fAF6qfPHMvRU12+MD/Vmc4wMZYlBYHunIO0YZlbBDAsCG07tWerv/OyM1l+a267c7gkhJe5XVGW8AX8siV1GYl4A11TqBg29dXbD/qfqQDptPEr62bj/JpRLPtS35il+ptiKoJWYKq1XQOEp2CtOA7iY8AxEXkNOB/YQpH4v2/tCPyg43xxvNlVqSERwg+c12xB9dQ7u/wIibQhMmFu4fRqRldW5P0x+dfGUdKzxp3Opj3tWY8LO3LnTh4ZqIYK8OqW/dz9pTre3HYg7+w9SuBD9GueEvxk7Wg27DzKic5U7pPjCJTtB47xmdnj+HD/RzQdOs7yt5sDJbujrlU3aSRVw8pCZggT2dcYHJ9A3AQoD1uh2I1x7MxtzwRVv+OQ/wx2WX0nehruGGd8+Sp59tbPYJ/bfhbQHS4ahYBTmHCIKgEoriKoB2aLyAxgJ3A9jk/A5mngH0WkDKjAMR39/0UcE8+Hwjrf/OBgoMRxFL1aggxhUgXovnwCdvuBYzxw09lcOmc8339uI+2ncgvNi2c74X8Af7h8XdZwTg/BmW1PGHka5cmjgUStrpRj3x1Rkcx73bgftufTsJMGsx0XNptFrXzskt2PrmnKuN/3Wo/QuOto4DU7dDEsCMOKIW6tnLDfwcvV2Hn4hB9HH14xRJXGKIQ4K4qerjriKBgvH8Nb/Xjn9sqbPLG2xdcEYbModH9Xh6oSgCIqAmNMl4h8E/glTvjow8aYRhG5w91/vzFmk4j8O/AeThDIQ8aYDcUaEzg/HptU2vihi9k+yJFZGrwo8TnwUQdL//dvSIZMN9k42Znih89voqGpLedszOO8mlFs2tPOS5v2UpZMcOH00f6qxYvr/yifEiB+foXBiWzKt1A0OFVhIXdCoWe3nz+tmurKikDCm3eesPlpXu3owLYn9NpPdPo9H1ZvP9ijGfuciVV+Nu2KtS08Flq12CuGVNpw3cKpvoO0N3k5HVnGF5W7471u94sIb8dRMIF8jNC5wVnFgaPULz97Aq9s3udHCVUknYKEuSK2hgJFlXDGmOeA50Kv3R/a/lvgb4s5DpsvnDMhw0l4qjO6kQTAA69+wIv9VBZisFCeFC6YOroo0VBxlACQ99oJuoV2RVmCuindjtuOrjS7Dp/wZ+OCE3oZ58rZTENRNO48QlKCZoPwNsCWve1cMHV0Tif9zYunA/hRQ9+75ly+99zGgPLypjDeOep3tPmTGCCjP8TrWw/4JrY4M/YowTl59HC6XHNVuNGQ3W2uN6Wlqysr/GeeNtGJV2E/iB1h5jm2w5FT2ZoF2eNcvf1gRja7fW5P+XWlDImEcNmc8dxxySy/3MTcyaNylpsYKpTcVPdYR+as0CszYf8QIL5A+Dhxfo1TGO0nb3w40EPJyrQzKmlpOw5uqOpX59ewdF4Nj9e3+Ot2O/TT4NjY39h2IGe57zI3m/knr2/3w2Cjwko93ms9QnlZgnk1o+joSjN+5Gn8KmLSkKvmk8db2w8GooaWLanjZGi5cfUFk3luwx7/+2lwJjGr1rUyefTwyPj+qBk7ENkiNGrWHRbAdhE6r3d1bx20bcc7Akpyw67MTPGwQzg81sfrm/37P9Xp9D647sLaQPnzN7dl1vwJZ7Pfc/VcJ//EPXcqlebzZ0/g1+/v80ueP3LLIv7mmnP95zhY+g73hsRAD6C/eac5c6b55fMnZ5SbLUUlALDz8AkeeG17YJY0ceQwPjt7bNGuGbLW5eXTs8dSUZYgKY6TzovtHl4e/XVOAFXDy7n9MzMDr589sYqKpDj9lV0hcEXdRJJJ5zyScPYJ2UPgUqk0l84Zz9PfvJg7LplFRVnCiV7KMf7pYyoznuf4kacFBMrzG3b7JgmAM8efzsIZYzIM1AZYsbaFrXvbIxWWAIiwv/0Ui2aOYfOedq574C3+7pebufGh1TQ0df8ePKGftBLCPAH8x1+YU7TZ7qKZYyi3GvWsWNvCt3+2noamNhqa2rjv5W2BcYbHmkwmaNwVbJf6busR7v55I8uW1PHp2WP9lZ5tVoJuBfPffmsOj9++mBsuqs14DuOqhgV6aNvvj3pmQ5GSWxGcijDqPrdhDzc1tTmZplb9+lLkwEeZ8f+fO3sCB9qjm933BdmU7pTq4ew5cpK0a4v2zBwCga5Z0G0WCZMAKsoTgVnrg+6M/8ODx7j7y3MDcfBeuWlvJp0LgUCHNC+8Ml84qZczAI4SvO0zM7mibiKvb93vFDUToW7SSOp3HPJns9v3f5RR/sLDKzluM7qynEvPGscz7+4i5YY+vvz+XgzdPppTofpE2cIwoyJy+jJkdP60ar62YCqPrml2opRShuVrmnmyoRWMcUqCu+UjUmnHz/fdq+f6Y915+ASPvZ2Zxe4lgX3r8rOo33Eoa55B+P7CzwEyo7CyHTsUVwNQgoqgLGL62dGV5oFXP+DB313APVfPzahfPxSxbei9PY/gCKL+JCHw4+s/CeA7QF/atJftB47x6JrmQLXNcCtM+xzh4mNVw8v9/Z6gWDRzTKCRSDKi/IOHF530qVljeGPbAVJpw90/b2TDriMsnVdD2/GOjIlEmdsk6OCxDq6aO8k3PXjnqxpeHlAiqbThp2/tYNmSOh6vb+bd1iNu7RqndpEYKCtLgDGk0k69qyvrJgbCdv/stz5B465gsptXQdXDAK9u3tejkM2+rpB57bwaVlqZvF7Yqj9a65mm0oa/eGo9501x2qEumjkmkAXsEV7V5HIs5yKfsO/vchDFoOQUQdvxzsjXX9q0l2//bD1zJ48imSBvNMhgp6+GnwYeWdNcsPmmt1weqgprF4+DYLVN246dTHYLyGRCGF6eZNW6VoCMYz2n470vbekWajkUQHlZgq/Od5rdOOGG7li60ixf4zTS8fspuzP7z33CcS6CIzznTHQqiNoFzNpPON9JT4kY9/4er28O1Bcy7n+SCeHuL9Vl2OjtUts3XFSbEXkUlXD39g4nWXL5rd0O57AD9mRn2l+5eG0+82XzZhO04WAM+5hHblnEd55az8bd7d33mwVjvDLm6/nCORO4efF0HnLbnibEKd53+yWzAtfe5YbAvti4xz+23C2NcW2MHgmeArnv5W1Zy20PVSSqCuJgZsGCBWbt2rU9eu+ja5ozfhxhCokaGepMGjmMve2n+vV+E4CJiMW2SQp89yvncsNFtVkLsCUTwhO3d9eIenRNsy8EvQqZT6xt8WsJVZQlWH5rdzP4letaOdB+il+/v9dX+lFRPV52tVfS4YaLarnv5W38/QubM0sSC/zxF+ZQXVkR6BMQFdHyYuOewAz+C+dM4NI5453jXHNQrufjFfbb136K8VXDIjuJhb/vd3x2JrVjRvA/nlqfMfbPzB5L7RmVLH+7mbRxrvGpM8dmPPfvXXNuIKM4SqhDpkLxzG/ecyhLCGkgZVVDDT+T3pAUqDqtjEmjhrPr6AmOHO/KebwIfGJCFVPPqKRx91F2tp3w9505/nQumnEGdVaEkPf8klmUzmBERBqMMQui9pXUiuDx+vzVMEtFCYAjRAqZB+QKf8zH2ROruGnxdDbsOsI7zW3+rC98zOa97aSNU7ah+eAxP5ImjEkbNu9p9wW7nfH6yC2LmDJ6OClrdm+bLzbvaeeJ+pYMO351ZTmHQitGr8SGXczMrvwpIggGY7pNEV6JaM+88fyG3RlmlHCfhRc27uW1rfv9Eg5hAVyWFBJuJctkMsFj9c2BhL8VDa3c/aW6wCzVjsbxTFCeEP/OU+sDYa5vbjvAmlBiWN2kkbyx9UDgM3+8vjmjx3O4Y9e182oiE88SVu5BYHXnVkP9zbaelwAJkzJw+EQXh09kz0q3MQY27WmPzGLftu8jtu37KPL7nzLOZ/fSpr18/uwJ3GEphN5mS/cnJaUInLLTmaFppUI4Dj1OopaNwZkBnTWhii172yPfP6qyjKPHuwI/mASw5PzJzJlYZVV+DZIAXwmAkzzlOXW9sU8YOYw9Rx2ndRr8WjsrQ1UivR9WebK7KYxdaiGbM/fwic6MPADfSR3RujA8G66urAjUrvdqCQ0vT/qJWMlkgndaDrMhlCUM3T6LqMqjX18w1W/T+ermfRm1iDpDtYEeuWUR1ZUVgUgaz6l9w0W1/nNr3HnEz7+ww0y92Xv4KTXuPupH8HQ3YJFAVI3g+EU6U8YvHW7cUdgF9+znLJDh5xhs5Pq5pA28uHGvX80Y8mdzD3SNJpuSUgQjKpIDPYQBpdDZfHnS6c277+hJ3nW7gKUNjK0axua9zswpgVMK+sP9H/kx3H9zzbk07jrCirUtvjPTmynblV8FpyDcr9/f5wjm0ADTIYH8lQum8NAbH1rVHw2r3CYp3qGSEHYedpb1y29b7Hcn80wn4XaY4Qf0+XMmZLShFCGjxn2UgzBb7Xov2/nzZ4+PLF3shah6z8nzaXgkE8GKlv/wwuaMoScS0bWBvFlsgmBFWNvmfeNDq32lJRCI0894RG59I+gubIdxkq3EbdRUN3kUK6QVcF5Pgv898J7Ltr3tAWVWN3kUN1xUy56jJ4d0YyevmjFkFv6Lky2tiqAfyNceUOkmIfBXX3Zs4j94bpOvCAyw53C3/dTrwOXJcW9W+zfXnMu17gzWXvaWuyYVj8MRUTYeZZZwE6D9VBf3XD03MPM1EGhkb9JO6OET9S3cc/Vcvucm/ngsmjmGZEICKwIvT6CiPMHtl8zi0jnjM+zo+cpqh3/Ubcc7mDJ6uN/FqyuV5r3WYJ9iwcmDuHnxdBp3H+WquZOYP62alSFF8LlPjPef3+rtBzMUuu1fCDtvh5XnLs9s19R5sqGV5W83s9JyensKIqrBiu0stsN5V28/6IfgptPGz0b23hfuPGd377r3+k+y6/CJnA2eqk4r45NTR7Np91HajneQSmef5JxekeSjUBLpGZXldKbTtJ/MX3KkZvRpnD15FGu2H+ToyaCf4Y7PzuSDA8f4lTVxsKsZ5yuNPRAtKbNRUoogbqlgxRHsjW6Gpx25ArDVasySMviNYcINw6Pis+/+Uh3/9PJWWg+fxOBErZQnBUk7oZEGIe1G/Nxy8Qwe/s0OPzR0xdoWrr2thsdvXxwwy6xyY7xFugW8XaY5PIZ7rp4bsJF7TVW8Wbd3vK1w8v1Is/2oPV9C2uCbtaDb4Ws7IOt3HGLOxKqM2lazxo7IuI5nkvEc2BCd6Rs2YX37Z+sDKyTvmXjCO22cVV3jriOR5q9s5w7PZLNlI4dDfcU91rtGQ1NbICeiPOmUdbDrNH10sovV2w/60T6b97Tz/IbdToXW4eVUV1bw8uZ9/Pr9fRzvTFFRluDSs8YxtmqY/xnf9/I2/vaXwZXVxJHD2Hv0lD+2ZEL40TfmMX9adYbj/SsXTPYjqBqa2vySE/bKLV9+wWDKQSgpRTByWBnHI0pMDEXKErDkvMlFXUY/Xt/MtfNqIm3WYQQn4zdXFUbPqXsylPh1zqSRfKFuIrsOn2D5282+c7ZqeDlfnV/DcjfRyC67HFVp04vWsU1HUcvtGy6qpXHXET+BKZ02TBk9PHCcZ0cv5Ee6dF5NpDCIinpCxF8xhc0DYcVrb+cSHuEQR2+/95odgruiodWPogJHwZQlE0GlO6+GOy87M3B+m2zx89nG2NDUxq7DJ7r9JVZzG7vQm90HwFPQr23dH8gx6EgZHl3TzAor6cwLFNi8pz0wS+9KpTl/6ujAvdgKFZzJwB9+/ixfKXtK1hu7p2zDvRjyPYc4OQqDIdqopBRB24noHILeRMP0N9PHVPKpM8f6jsNikkrj17H5ygWTeebdXVmjqhIJ8atdQlBoeDOmDTuPRNqdr7uw1g8Vjcrg9Gb8iYTwQuMeqisrcv4Q883kPWdneVICPoxwBEfcH2nY6bfU6v41f1o137r8LNZsPxgwC6VdpRa1kqiurAgojqvmTgpcL9e4ohyQgJMrkSWKyjtnlNLtTbZw+DvwDbe3bzIpXLew1n9OYd+K/Tw8pWpXQvVKjHumSKy/V65r5Yn6lsD3NCGZzafmT6tm+a2LMmbyuZT/DRfVZkRMfVwoKUWQTdjb0SWDgZGnlfn2yITAuVNGUTdllP9ltcsN9yVRCnHF2hY63M5jF585lt98cDDDxCbiCI77X9tOQsiIHQ9HCjkmZ2HUaWWcOf50P8kq20zykVsW8cCrH/DCxr1+EhEQ+aOcM7GKr1841Td/QLDAmi0oy5IJrls4NVIgFRLBkc/pN39aNctvW8wPn9/k274NTiJZ1D17742afdpEhR6Gx2K3rbSJskkvnVfjK92+tlmvXNfq/8a83A47K9z2reQqc+FlINsN5O0Ma4GAzykhBGb2NlEKdbDM0PubklIE50ys4p3WzPDREcOSdORJOOlPZo4dwcY97XS6wsrufGQLsoTnqe0jwkpgltUYPm3gtWzmIeuNXuz4d57eQDpt/GgWm3OnjKJx1xEOHe/0M1vtGHh7CQ/Oj3Pv0WCjlvte3pq3ZWJdRIlgW1CmUmnfJNSbKpJxs2zPnFBF/Y42/3E99MaHXFE3MVL45Jt9Zgs9zNW20ptUzJ0yKjIBrZg26/A31duOena5hLG3b6kViADBZDavgU7Yh6Jkp6QUwU4r2sWmbRApASCorNzZjSdM3m057NvYe1ocr2b0aYEyzVFUlCX4/U/PiMxCtfHCHru6nGxYb1XhVc4MK4FkAuqmjOI96x6jYuDDgmBCKAdk5+GT7Dx8kte3HqD54DGqhpez6/CJQCniB1/7wN/26tFfNXdSpNDuTQRHLgEaXoEkrDyFbD6MOGRbhUTlONjmtnztFIs1I752Xg0rGloDDmTvej1RPlGBCB6DxQE7lMiqCETkIpyG8bOA9cB/NsZs7K+BFYPDWeoMDWa6Uk6s/MqIolpxiGqUsvtIbiVw5rgRXDRzDHMmVnHbZ2YGknymnVFJ06Hu2vpXXzCZmxZP901Vdpx/FHMnOyauJ12TE0THwId/wDOtyJkwD77ujC8h3YsTAzQdPO5vp4E3tjr16O1QR9v00BsBkk2Ahlcgdm37ONFI2ciluMJjGQyC0bPJ53Jy9+W1VAEURq4VwX3AnwCvAV/G6SX8W/0xqGJRlhQ6h1gIqbjCLaq6Zj68cr2Nu46wZvtBPth/zJmt5znR9gPH2H7gGCvXtfLILYuoHTPCL4DW0nacZALSbuz2vzfu4abF07nzsjO57+VtGauUcBXU6y6s9W3mXrKXZ8LJZVp5KEejHO9+wgskL//A+9t7jna8u4cnoMJmqWzELQ0QFti3XzKL2y+ZFchEhsJbHBaiuPpLMOZ7JiqgBy+5FEHCGPOi+/cKEfnz/hhQMRliOgDAt4euWteatxhZGK/Xh72aSAiBmi8CTB59WqBZuvecvNn5nZedSdvxDr8UgVjP0Z7BR/VzuNzt17v36Ek/Osi7L1so5IrWsEMKPb5ywWQOHuugbtJIvx5R+PP17tV+b0YrQjdZqisV30lcSGmAXAK7t+UFBpNgHUzlEpTCyaUIRovItdm2jTGrijes4lBRlohsTDOYWed2VPNq0x/86FRO+36ZlTWbShu/4JlXZuDTZ47lqrmT/EgeA+xtP5VhQkoIWW3o4UgNO4HMz/xNG8qSwitb9vtC1osOiiKXUFs0c0zgvgSYPaGKe63Z+7+tafL7+wowyzVv+Q7jLK0InbLTuc1SYWxzz6lOJzKn0Bn5YCov0Bd83O6n1MilCF4FvpRl2wB5FYGIXAn8CEgCDxljfhDafynwNOCt+1cZY+6JM/CecOPC2kFT1CqZIFA9Mhtdabj/1Q94dcv+yBj8MEvOm8Sz7zltDsvLElw1d1KgO5OX8LXBTagCSKcM59Y4DlxbYdjJYVFOyKhZrp2I5SWIhYVDrpr0kGliCCiYkG390TXNGZ+pwTFvtR4+wbXzaiKbkuQrnxA1Dg9PMXW48exPNrQW3Lw9qi9CVB/hocJgKpegFE5WRWCM+b3enFhEkjh+hiuAVqBeRJ6JcDi/boxZ0ptrxaV2THaHY38TpQSmVA8P1EH32Hf0ZE4lEDaTpN0iYMuW1GXNkJ07eZT//jSweOYYNu9tz1AYNrkiNaKOa2hq8yNFksnupC3bLGPXpL/7y3P9Qm1hU022+3h+w+6M6zux5EHTVnjc+ZRaLlPH/GnB1oqpVOEzYHsMdp3+oWpWKWboqVJ8coaPishc4E+BOpyJ1kbg74wxubu7OCwEthljtrvnegy42j3HgPDwm9kdjoOBukkj2d9+KiP5apTVXjHMwunV3Ou2dLRj4YXu+vlRpomoWvVF+SF7/gL3/w+8+oEf/hquSe/lHnivxjExhMtfJBOQFMkwW4XJp9TC5p9VlvmnocnJBSgvS0SuJOLijaE3OQyDicHks1AKI1f46NXA3wHfB/4eR17MB1aJyJ8YY57Oc+4pQIu13QpcFHHcYhF5F9gF/IkxpjFiLLcBtwHU1vY8OWSnFfY42KgoS3DpnPGMqxrGPrdzViqdO5ErgWMrb2hqY/60aqorK5wkM5NbCIKT1WqHWlZXVvT5D9lz8nolC7zsYA9PEXnYOQdefoJdjCyq69WimWP43jXnOkreGH7/4pkF1wiKIlvtHSCworl+YW1kclah11KzijKQ5FoR3ANcYYzZYb32roj8Gseun08RRKW9huN21gHTjDEficgXgaeA2RlvMuZBnJwGFixY0OPYn1NxjPIDwGdmj6Vu0ki/vWEyIXn9B0lx4u/tssH3PNsYMAtlE07hcEwhf5nlOIRt6mEBF84OnjX+dJoPHXdNR1YHrkRmH1l7ht7RGUxAW7akjta243R0pbnn2UYeuWVR7DDQbMyfVs0lZ43jRVdxebV3oDtTN5U2TA4Vq+vptdSsogwkuRRBeUgJAGCM2SEi2W0V3bQCU63tGpxZv32uo9bfz4nIP4nIWGNM3/Wss/AcfIOJBI55w66amS0hq8wtzexl0dqOWLsdom0WimLVutbANZKJzKJcUFgbvWw2dVvAvdi4x+9rAPD7n54RmL1DtAMagrNmCXXEimoF2Vth2tDUxqtb9vvb9jMqxuxdzSrKQJJLEXSKSK0xJtDoV0SmAXFqMtQDs0VkBrATuB64IXSuicBeY4wRkYU4crFoJTXLBllxOQH++ppzacvRnMXjC+cEG2SHK3WGo4OyCaiGpjZWrO222HlN2cNCqNC48FwlDzzH8U/f2uE0gRG47TMzAzkFHrmu4ZV5nhtKQMt17z3tCevV54fucsje+3X2rnzcyKUI/hJ4SUS+BzTgmHUuBO4C/nu+ExtjukTkm8AvccJHHzbGNIrIHe7++4GvAv9FRLqAE8D1xvSwgE4MhpUlON4xcOahsoQTDgqOE/jys51kK69Gu1deN0xC4Pypo3NGvthRNF6nqyjC9d6vu3BqZFGufHHh+cxAYUXknc8LTz16qit2uGRUmedw1M+182oyGq70JskpfD/XhkpLqwJQPk7kCh99SkQ+BP4b8Ac4cqMR+Lox5t04JzfGPAc8F3rtfuvvfwT+sQfj7hFjTj+NtuMf9dflAtzx2ZlcUTeR+1/9gA8PHGPHgY94ceNeXti4l4RAWTLBgumjqQ+16EtA1po0tkDymr7Yna6ihFVYwNm183MdF55l5zMD5TLtJBMSGSKajbBS8nokeGOyx2IL7N4kOandXiklcoaPugL/d/tpLEVn7uSRflnl/kRwZsCb97TzutVpycMrSHZaeZKE4Nr5ncbu508dHasmjV1GIpfQiyvgch2XzwxkY68cli2p4/kNuxlenvQbxHtVQXN1NgtnNa9Y20JX2pAQ4XOfGJ9V2Pc2Gkdn/kqpkCt89GJgpjHmX93tJ4Ez3N1/bYz5dT+Mr09544Oi+KDzYoDla5pJJhwnp6cEAuGTIowZ4YZ/4kTD3H7JLCAYrhiOpgFH2D7Z0BrotZpL6MUVcNmOiytgw8ljXk2fsoRQluwuXf3mtgN+m8Fs5idPKe08fILHXCd52hh+tWkvZcnoeH6d1StKPHKtCP4KxyTkMQe4GRgBfBsYcorgyACWofZq9CcSgmBIJhOcXzOKtW6jklTa+P2Hk4If/nnfy9u6a+q7fVq9qqB2aKXt2Dx70sii3ktcARsI+Ux59T+de71u4VRaDh3nja0H/GY29mw+yvx052Vn0tDU5rYidM5lDHx1fg1TXFNRvmxoRVEyyaUIRobKQWw1xjQAiMj3izus4jAsmaAz1b/N6wUn29UYJ0HKS4RqP9HJg69vj3QOpwxs2HWEhqY23m05HDgmyvRjN+JOG3iv9Qg3PrQ6su5+XML1gMK9XeMIWDspy8MrZrd0Xo1rKnNWaWnjJLV55DI/2YXtKsoTBdf5URQlSM7qo/aGMcauRDqhKKMpMkmvLnN/Xc/tBxDOdG1oauO6B97KWRb7QPspbnxoNac6g1FO4Yxb6J6h3/vSFt7YesCvu5+v61c2wl210sb4fWYff7uZ737l3EB7yPC92dt2Q/QE3f2XIVjmIkEwqS2X+Smq7lBUmGhPQ0cVpdTIpQjeF5HfNsb8wn5RRJYAm4s7rOLQ1c85BJ/7xPjIWPnV2w9mtHCcPqaSnYdP0JVyykOMqxoWaEYjQHkyu49g9faDgXh6serwd3QWFjETno0HykAYWPb0Br+kdLjsQ7h4mt0QPZkQNu1pZ/3OI6xys6GHlWfvspXL/GSbxTbvac+4bnhsQ7GQm6L0F7kUwR8BvxCRr+KUggCn1tCngH6pFtrXlJUJ9L6SQmzGVw0DupO/vDj36soK3JJAPi2HjnPrZ2bSuPsoV82dxJyJVX7CWDKZ4KvzayJNII+uafZLUwwrD5qevPLMaYJml3yEo3TsFQE4Nv6V61ppOXQ8EKkUleF752Vn+gI9nA3ddrwjr7DPFoVkV+y0G+141wX6PNtYUT6u5Moj2CYi5wE34lQfBadt5R3GmNxNbwcp40YM40g/Nar3Ytobmtr4xoNv+RnNT6xt8csk26QM/MT1GXh9db1M2mw28IamtkBpio5OR7h6bSPtUNTnN+zOmlvgncsWyOGErR8+v4m33RwHAzxe34JxI6ASkDPD184utrOh7X4DcfBMVqc604jYz9D49+pFTG3e0+5HYGkhN0XJTb48glPAw/00lqJz+dkT2La/OI1pEjhF4LxaQJ6Qu+/lbW4XLIfOlImsxgfdHcJOdab5H085lb69TNooVm8/GChNkXCFYENTm5+t3JV2avJ4jdujTCT2qsIrOXHDRbWB486cUOUrAuiuFJqQ7q5nbcc7WLakjg27jkTeY2/DOVdvP+ivQLzbTuDUYDI4DXYQ8U1F3v2EC/Cp70BRguRUBB832k8VZzWQEJg0ejjnTBpJ7ZgRbNjl2MDBMcnY+QJOZVG74JvTCL4sKQj4gtsTdOGwShs7WighjgAHAo7ec6eM9DuPZSsVES545/kA7OMOtJ/KuL7gCOHh5UmWPbOBVMoE7iMc5gq9C+dcNHMMyVDLyk/PHkvtGZUsf7u7SczzG3b7CiOdDhbg0966ipJJSSmCLXvbi3LetIGdbSfY2XbCL1sM8Fh9CyZt/IbzCXEcyC9t3OubVK6/sDZQLuHel7YEGq0kpHuWH57FRs2w7SYnqVSauVNGBTqPRdUACjuuvZLLtoAc5/o7bCZXD2ff0ZOBHgOB1U8f2+YDoaNuNNS3Lj8LIGByqps0sjsslXhhqYpSyuRVBCLyX40xP8r32lBg9+HMNpDFJCxgjXEcyHakTN3kUYFOYuGOW7dcPAPIHgFjR89AdLG0a+fV5KwBNKw84XcNg+5GNbbyuXZeDY/VtwTuKaqtJjgJcZAZ5pqPfCabhqY22o53+A3o7eMeuWURq9a1sq/9FC9t6lZMCYkflqoopUqcFcF/wmlAb3NzxGuDnoEuQJ1ICHWTR1E1rIx/b9zDBVNHB8IevVo8fmy9OC0kc81io0wdUXb4fDWF7vl5Y6BXwM/+o5X1O48EzvtddzbuRehEUeH2HrYFdTYBH05ai1J2UVFC2Uw6K9a2ZJQZLwuV29CyE4qSSa5aQ9/A6R8wQ0SesXZVUcSeAcVkyujh7DxcnICncyZVUVNdya/f3xsoNY3BNw2l0oa/+Nl6X4juONjdOtNOAPPMRnbV0Wyz2CglEW7Wnm+mPX9aNXOnjAooAq/0BQTP64W1evV+PBZOr+aSOeMjhX02Ae+9nq14HBA4JhwiGs7N6IzIE7H7CNj3qwpAUbrJtSL4DbAbGIvTs9ijHXivmIMqFu0ne+csPqOynLbjnZGz4UPHOrigtpol5032awalDUw7o5Imq1dyrlVJVygSx67ImW0Wm8/UEdc5Gjb92ONMJjNDQQ+0nwr4BmZPqIpsDxluAn/vS1u4au6koEM3S/E4+73GOBFAJks/5kUzx1AeajwULkutKEo0ufIImoAmEbkR2OXlDojIcJy2kzv6ZYR9yM5e+ggO5Shat+foKR5d05wRNmkrgVzYs+uyZCKjLHO2WWw+U0dc5+j8adXcevEMHnh9eyDRTXCKuoXfc/sls3hly/7Ixi021ZUV/r0Z4PWtB3h964GMxvVRxeM272kPvDccmhse//LbFrNyXSsH2k8xrmpYr5vKK0qpEMdH8ARONrFHCliB061sSJHoh1JDvfVDZBO8NmFTTy5TR5wVg2eD/+lbOwj3h0smhKVuYlxY2Xxtfu6EN3CK50Vh3Ht1c74ii8fZtYgEx1+Sqym9mnwUpWfEUQRlxhg/7MIY0yEi8esVDCLKkomiX6MiKXzx3Ek8/c6unA7VC6aODiRogSPshpVnTyCDwuPgc60YwnZ6OznNqZqamZsQrikkIhxoPxXop2yf/8mG1ox79AR7eVmCu7+UvUJq+4luM5xxtxVF6XviKIL9IvJlY8wzACJyNTAwHV56yejh5Rz4qDjFhiaOHMa5NaO5wxWIC2eM4TtuhI2HVzTOE/Rff+CtwP6LZ3f7BbI5eHsSB59tpmyfCxPslWDXNrJzE8I1hTCGFzbu5ZUt+1l+66KMsdp9Eq44ZwJjq4bxxNoWUiknay5b2YuGpjYeeuPDwGsPvfEhV9RN1Fm/ovQxcRTBHcAjInKfu93CEG1f+fsXz+TbP1tflHPvaz/Fq5v3IcDYqmEsnVfDE7cv9mPbx1s264amNifz2AQdm7YS8Grq2CUfoG/j4BfNHENZQuh0M4LDYZ/2cXa/4eHlyQxfSJRSCo/19ktmOWUx3OifqMQ1j3D5DHCcypoApih9T15FYIz5AFgkIqcDYoyJnZ4rIlfi5BskgYeMMT/IctyFwGrgOmPMk3HPXyhe6eS+ZPTwMo6e7PK7cHmRNE+ubWH5bYv5m2vODRxvC3m7xLTtF7Br6oRLPmQz9YRj8mPHyXtGepGss3PvmivXtfJkQysvbdrrRPCkurOmo5RS1Fj9YnBZon88PCXiPQchGE6rKErfESezeAzwl8DFgBGRN4B7jDE5cwlEJAncB1wBtAL1IvJMqOuZd9wPgV/27Bbis3Jda/6DCkCA3z5vcqBxvEdnKnr26pljbCUQ9guEa+qEZ85hU0+4kQzG0JXO3pDGUxq7Dp+gK5X2a/Tkm223HDpOV8oxCUna8I2Lan0hna1HwqKZY3wHb0NTG/c820jaNUOFi8HZ2EqkurKix53WFEXJTxzT0GM45aeXuts3Ao8Dl+d530JgmzFmO4CIPAZcDWwMHfcHwEr6IQqpL4KG7LwAA1QNK+ORWxbxwKsfBOLqk8noBvJ2oTgR4fOfGJ/haJ0/rZpbLp7h9xPwSj5AdHJY2G/gvSdbkTm7oXy2xu824VWM124yW3hm2KHt9UjYdfiEP04hWAwuilxRQFpBVFH6jjiK4AxjzHet7b8Wka/EeN8UHH+CRytwkX2AiEwBrgE+Rw5FICK3AbcB1NbWxrh0NMd6UX1UBG53G8fYuQGNu49y1xfP5vypo3nRLSYH8PWIjFZwhNuyJXV+qYbXtu7n9ktmZRxXNbw8o41jtoih6soK39xSVuasCFJpx/RSXVnBfS9v8wWmrTS8JvLZGr972KuYBJnJbtmOTxunR4KXMW0rnmQywc7DJ2hoasvpHI9CK4gqSt8SRxG8LCLX4+QTAHwV+EWO4z2iJuDhiMp7gf9ujEmJZJ+vG2MeBB4EWLBgQY9D9d9pOVzwexLg38lP39rBzYunB4rCXTV3EtBdvM1zjOYKAW073uGXksgW+VNdWUEyIX6D9nCmrV2GwTa33P2lOr8MxIH2U9z9zIaAmSjswI3T+D38niglYAty+3hxw1JtxSM4dYEee7vZb1l59zMb6EwZypPC8tsW5xyTVhBVlL4ljiK4Hfhj4P+620ngmIj8MWCMMSOzvK8VmGpt1wC7QscsAB5zlcBY4Isi0mWMeSre8Auj9ozKQH2fOKQBMd2mlqrh5XzvmnN5fsNurpo7KdCTOG4xszhJXp5wl4Twmdnjsr7PFoq2uWVVyG8R1Toyrlkl373lKnznFYuzFc/q7Qf9vgudXWker2/2S0N0pJw+Br15foqiFEacqKGehtrUA7NFZAawE7gep4idfe4Z3t8i8lPg2WIpAehZ1m9SnFo7th19/rRqXwHYxM1sLaQsBMbw0sa9vL51f9bKotmUg+2QjmodWQi53pOv8N2ciVU5xzxh5GlAdwZyPl+OVhBVlL6laI1pjDFdIvJNnGigJPCwMaZRRO5w999frGtnY8yIwhOib/3MTK6om9jnQidKsNrlHuzQSduEFK4smk0o2s3nszW+z0ahjth8M/TwvYbHDMSqW5TrnIqi9Bwx4eIyg5wFCxaYtWvX9ui9N/3zmoB9Pw5/+ltzcta36SuiIm0adx1hxdoW3/FbiFPUS1rLVwso3zjiXrO3UTwaBaQoxUVEGowxC6L2lVSryp7UqrHbHBaLhqY27n1pi78C6OxK03a8g7+55tzI7mJxhebKda10dKVZZfUOtlcdUbH5PXXE9naGbr9flYKi9C9xEsr+rzHmpnyvDQU27Sm8Z3G+WPfeYs/AvfBMz7wSJRDjztizRRh57/VKHJUVsYSFN95ChLqGhipK/xNnRVBnb7iZwPOLM5ziEq5dE4fergjyCUJbYNsNaSC6dWO2Gfuja5p5vL6ZCSNP4/ZLZuWNMPKIW8KiJ/faE6GuoaGK0v/kalX558C3geEictR7GejAjekfaoweXs7+AquPZqunH4c4gjBbjH644qcnEBfNHENZstsRvGjmGB5d02wV0zvCy5v38dhti7M6kcPKIFzMrRAzT66ewnbNpI7OeEJdQ0MVpf/J1aHs+8D3ReT7xpg/78cxFY0ZY0cUrAh6U5Yizuw22ww8p0D0Vjbu/5/fsDtwTq/OUa4Io/YTnTz0xoekjelxMbdwP4NwT+Hqygo/hDUNvNty2M8kzkauFYn6DhSlOMQxDb0tIqOMMUcARGQ0cGkx4/2LxagCzTy97Xkbd3YbNQPPJhC9ZCy7jPNVcycFoqHKrTpHubqZ1Y4Z4SfG9USw2jP+qJ7Cq7cfJCHdbThf3LiX19x8iHzKIE7SmioDRekb4iiCvzTG/MzbMMYcFpG/BJ4q2qiKxM62+FnFn4nRJCYfceztuc4dJRCjlIt3jO0jyGej97KXO7rS1O84lLUEdS7sGX+2nsLZ8iEKjRBS34GiFI84iiCqv+OQDDs9dCyeWagsIRlNYno6E81XQbPQc2dTLjdcVJuR7ZxLePaFYG073uHP+BOS2VPYG+uqda2BfAh7tRL3/tV3oCjFI45AXysi/4DTW8DglI1uKOqoisSimWN46p1wuaMgXjhlXwrMbBQ7Zt+uShoWnnGb2ueaqccRzt5Yo/IhCrl/LSuhKMUjjiL4A+A7OD0IBHgBuLOYgyoWlcNy3+4V50zwew57FHMmWsxz52sCk88pG2embp+jurLCz1XIdmwcM1cutKyEohSHOEXnjgF39cNYis62vbkTyl7fup87Qr0BijkTLUSQFkq2qqTh68dNRss1U4fonId86CxfUQYHufII7jXGfEtEfk5E4U5jzJeLOrIikM9HkCvEs1hCqjeCNBe9WW0U+t5sWcxxBLzO8hVl4Mm1IvD6D/xdfwykP+hKZ+izANmEXrHj14vhh+jNbLvQlUpYcVRXVkS2qtRZv6IMTnIllDW45SRuNcb8Tj+OqWgczVF0ruq0Mn76ewsHJH69WL6C3sy2C1mphJVOtlaVGv+vKIOTnD4Ct4XkOBGpMMYUt/paPzBr/Okc2tEWue/GhbW9tpf3lMFqK49z7/ZqyQ4djWpVqfH/ijI4iRM1tAN4U0SeAY55Lxpj/qFYgyoWw8uTWfe1Z2ls31/x6/nyDfpaSfRFeGi21VLYtGS3qtT4f0UZfMRRBLvcfwnAa1s5tLrZuKxrjl4NAGzJElFUjNl6IYK9ENNU3PP2JDw0X0vN8GzfVmxRrSoVRRk8xFEEG40xK+wXRORrRRpPURl7+jA+OhVdZqJ+RxuPrmnOyM7t69l4oT6HuKYp+7xlCeFrC6ZybZbOZIWGh/Y221cjgxRlcBNVPiJMVOXRIVmNdPaEqpz7w1U8PeH69y9s5saHVtPQlH1FEZdsoZbZ8IRtUrJHNYXP25EyPLqmOeuY454zH96K4Y+/MEedwIoyhMmVR3AV8EVgioj82No1Eog2qGee40rgRzjN6x8yxvwgtP9q4Ls4VYq7gG8ZY94o6A4KYHzVsJz7r5o7KbBdDEdxT7Jp45imvPPmKvBW6DnjoLN9RRn65DIN7QLWAl8mWFuoHfijfCd2Q0/vA64AWoF6EXnGGLPROuxXwDPGGCMi5wFPAJ8o7BbiU5WjxMT3rjk3wywUFR9/38vbei08l86rKaipfBxh6wn3letaebKhlVRKzTWKosQjVx7Bu8C7IvKoMaYTQESqganGmDg2koXANmPMdve9jwFXA74iMMZ8ZB0/giI7oV96f1/WfXMmZpqNoqJfepNPEPYPLO1Fr4MoPOG+NKLAm6IoSjbi+AheFJGRInIG8C7wf9xqpPmYArRY263uawFE5BoReR/4BfD7Mc7bYw7naESfzVY/f1o1d152Jm3HOwqy7We7Rm/PEQdvzKoEFEWJQxxFMMoYcxS4Fvg/xpj5wOUx3hfV5TGqZtHPjDGfAL6C4y/IPJHIbSKyVkTW7t+/P8alozmtLPvt5rPV94WDta+ctIqiKH1JnPDRMhGZBHwd+IsCzt0KTLW2a3D8DpEYY14TkVkiMtYYcyC070HgQYAFCxb02Hx0zuRRtB4+mfH6iIpkjxvCFMJgyiDW/r+KonjEUQT3AL8E3jDG1IvITGBrjPfVA7NFZAawE7geuME+QETOBD5wncXzgAqgOPYS4NI543lh496M129aNC3W+/vCwToYnLTa/1dRFJs4/QhWACus7e3A0hjv6xKRb+IokSTwsDGmUUTucPff757nd0WkEzgBXGeMKZrD+JXN0c7iK+omFuuSgxLt/6soik2uPII/M8b8TxH5X0Tb9v8w38mNMc8Bz4Veu9/6+4fADwsacS/YezTTLASUnCDU/r+KotjkWhFscv+/tj8G0h/MGDuCd1uPZLzeX4JwsNjlB5OvQlGUgSdXHsHP3f//S/8Np7h8eOBY5Ov9IQgHm11+MPgqFEUZHOQyDUW2qPQYiq0qJ4w8DchcEfQHapdXFGWwkiuP4O+Avwc+xHHk/sT99xGwofhD63tuDzWmB6hIRqU79D2aQ6AoymAll2noVQAR+a4x5rPWrp+LyGtFH1kRmD+tmrIEdKUH5tpql1cUZTASJ49gnIjMtGoGzQDGFXdYxWPJeZN56p3uvLYvnjspx9F9i9rlFUUZjMRRBH8EvCIi293t6cBtRRtRkbn3+k8C8MqW/Vx61jh/W1EUpVSJk1D27yIym+7y0O8bY04Vd1jFRYW/oihKN3FWBLiC/90ij0VRFEUZAOJUH1UURVE+xqgiUBRFKXHyKgJx+B0RWeZu14rIwuIPTVEURekP4qwI/glYDHzD3W7H6UWsKIqifAyI4yy+yBgzT0T+A8AY0yYiFUUel6IoitJPxFkRdIpIErfukIiMAwYgN1dRFEUpBnEUwY+BnwHjReRvgDeA7xV1VIqiKEq/ESeh7BERaQA+j9OQ/ivGmE153qYoiqIMEWIllAF7gdfd44eLyDxjzLriDUtRFEXpL/IqAhH5LnAz8AHd/QkM8LniDUtRFEXpL+KsCL4OzDLGdBR7MIqiKEr/E8dZvAEY3ZOTi8iVIrJZRLaJyF0R+28Ukffcf78RkfN7ch1FURSl58RZEXwf+A8R2QD4VUfztap0Q07vA64AWoF6EXnGGLPROuxD4BI3N+Eq4EHgogLvQVEURekFcRTBvwA/BNZTWP7AQmCb1dDmMeBqwFcExpjfWMevBmoKOL+iKIrSB8RRBAeMMT/uwbmnAC3Wdiu5Z/v/GXg+aoeI3IbbDKe2trYHQ1EURVGyEUcRNIjI94FnCJqG8oWPRnWFNxGvISKX4SiCi6P2G2MexDEbsWDBgshzKIqiKD0jjiLw2nktsl6LEz7aCky1tmuAXeGDROQ84CHgKmPMwRjjURRFUfqQOJnFl/Xw3PXAbLfZ/U7geuAG+wARqQVWATcZY7b08DqKoihKL4iTUDYK+Evgs+5LrwL3GGOO5HqfMaZLRL4J/BJIAg8bYxpF5A53//3AMmAM8E8iAtBljFnQ05tRFEVRCkeMyW1yF5GVOLkE/+K+dBNwvjHm2iKPLZIFCxaYtWvXDsSlFUVRhiwi0pBtoh3HRzDLGLPU2v4rEXmnT0amKIqiDDhxMotPiIgfzSMinwZOFG9IiqIoSn8SZ0VwB/Cvrq9AgEM4RegURVGUjwFxoobeBc4XkZHu9tGij0pRFEXpN+JEDQ0DlgLTgTI3ugdjzD1FHZmiKIrSL8QxDT0NHAEasDKLFUVRlI8HcRRBjTHmyqKPRFEURRkQ4kQN/UZEzi36SBRFUZQBIeuKQETW49QUKgN+T0S245iGBDDGmPP6Z4iKoihKMcllGlrSb6NQFEVRBoysisAY09SfA1EURVEGhjg+AkVRFOVjjCoCRVGUEkcVgaIoSomjikBRFKXEUUWgKIpS4qgiUBRFKXFUESiKopQ4qggURVFKHFUEiqIoJU5RFYGIXCkim0Vkm4jcFbH/EyLyloicEpE/KeZYFEVRlGjilKHuESKSBO4DrgBagXoRecYYs9E67BDwh8BXijUORVEUJTfFXBEsBLYZY7YbYzqAx4Cr7QOMMfuMMfVAZxHHoSiKouSgmIpgCtBibbe6rxWMiNwmImtFZO3+/fv7ZHCKoiiKQzEVgUS8ZnpyImPMg8aYBcaYBePGjevlsBRFURSbYiqCVmCqtV0D7Cri9RRFUZQeUExFUA/MFpEZIlIBXA88U8TrKYqiKD2gaFFDxpguEfkm8EsgCTxsjGkUkTvc/feLyERgLTASSIvIt4BzjDFHizUuRVEUJUjRFAGAMeY54LnQa/dbf+/BMRkpiqIoA4RmFiuKopQ4qggURVFKHFUEiqIoJY4qAkVRlBJHFYGiKEqJo4pAURSlxFFFoCiKUuKoIlAURSlxVBEoiqKUOKoIFEVRShxVBIqiKCWOKgJFUZQSRxWBoihKiaOKQFEUpcRRRaAoilLiqCJQFEUpcVQRKIqilDiqCBRFUUocVQSKoigljioCRVGUEqeoikBErhSRzSKyTUTuitgvIvJjd/97IjKvmONRFEVRMikr1olFJAncB1wBtAL1IvKMMWajddhVwGz330XA/3b/3+dMv+sX/t87fvDbxbiEoijKkKSYK4KFwDZjzHZjTAfwGHB16JirgX81DquB0SIyqa8HYiuBqG1FUZRSppiKYArQYm23uq8VegwicpuIrBWRtfv37+/zgSqKopQyxVQEEvGa6cExGGMeNMYsMMYsGDduXJ8MTlEURXEopiJoBaZa2zXArh4c02vCPgH1ESiKonRTNGcxUA/MFpEZwE7geuCG0DHPAN8UkcdwnMRHjDG7izEYFf6KoijRFE0RGGO6ROSbwC+BJPCwMaZRRO5w998PPAd8EdgGHAd+r1jjURRFUaIp5ooAY8xzOMLefu1+628D3FnMMSiKoii50cxiRVGUEkcVgaIoSomjikBRFKXEUUWgKIpS4ojjrx06iMh+oKmHbx8LHOjD4fQVOq7C0HHFZzCOCXRchdIX45pmjInMyB1yiqA3iMhaY8yCgR5HGB1XYei44jMYxwQ6rkIp9rjUNKQoilLiqCJQFEUpcUpNETw40APIgo6rMHRc8RmMYwIdV6EUdVwl5SNQFEVRMim1FYGiKIoSQhWBoihKifOxVAQicqWIbBaRbSJyV8R+EZEfu/vfE5F5g2Rcl4rIERF5x/23rB/G9LCI7BORDVn2D9SzyjeugXhWU0XkZRHZJCKNIvJfI47p9+cVc1wD8bxOE5G3ReRdd1x/FXHMQDyvOOPq9+flXjcpIv8hIs9G7CveszLGfKz+4ZS8/gCYCVQA7wLnhI75IvA8Toe0RcCaQTKuS4Fn+/l5fRaYB2zIsr/fn1XMcQ3Es5oEzHP/rgK2DJLvVpxxDcTzEuB09+9yYA2waBA8rzjj6vfn5V73j4FHo65dzGf1cVwRLAS2GWO2G2M6gMeAq0PHXA38q3FYDYwWkUmDYFz9jjHmNeBQjkMG4lnFGVe/Y4zZbYxZ5/7dDmwis8d2vz+vmOPqd9xn8JG7We7+C0enDMTzijOufkdEaoDfBh7KckjRntXHURFMAVqs7VYyfxRxjhmIcQEsdpesz4tIXZHHFIeBeFZxGbBnJSLTgU/izCZtBvR55RgXDMDzck0d7wD7gBeNMYPiecUYF/T/87oX+DMgnWV/0Z7Vx1ERSMRrYW0f55i+Js411+HUAzkf+F/AU0UeUxwG4lnFYcCelYicDqwEvmWMORreHfGWfnleecY1IM/LGJMyxlyA0498oYjMDR0yIM8rxrj69XmJyBJgnzGmIddhEa/1ybP6OCqCVmCqtV0D7OrBMf0+LmPMUW/JapzubuUiMrbI48rHQDyrvAzUsxKRchxh+4gxZlXEIQPyvPKNa6C/W8aYw8ArwJWhXQP6/co2rgF4Xp8GviwiO3DMxp8TkX8LHVO0Z/VxVAT1wGwRmSEiFcD1wDOhY54Bftf1wi8Cjhhjdg/0uERkooiI+/dCnM/nYJHHlY+BeFZ5GYhn5V7vn4FNxph/yHJYvz+vOOMaoOc1TkRGu38PBy4H3g8dNhDPK++4+vt5GWP+3BhTY4yZjiMbfm2M+Z3QYUV7VkXtWTwQGGO6ROSbwC9xInUeNsY0isgd7v77cfoofxHYBhwHfm+QjOurwH8RkS7gBHC9ccMFioWILMeJkBgrIq3AX+I4zwbsWcUcV78/K5xZ203Aete+DPBtoNYa10A8rzjjGojnNQn4FxFJ4gjSJ4wxzw70bzHmuAbieWXQX89KS0woiqKUOB9H05CiKIpSAKoIFEVRShxVBIqiKCWOKgJFUZQSRxWBoihKiaOKQCkZRGS0iPx/fXi+S0XkU311PkUZKFQRKKXEaCBSEbgx5YVyKdBjRSAiH7s8HmVooopAKSV+AMwSp77837oz+pdF5FFgPYCIPCUiDeLUqb/Ne6M4vSTWuUXIfuUWd7sD+CP3fJ+xLyQiC0XkN+LUlv+NiMxxX79ZRFaIyM+BF0RkhDi9F+rdY692j5suIq+711ynKw+lmGhCmVIyuML7WWPMXHf7UuAXwFxjzIfua2cYYw65pQfqgUtwJkzrgM8aYz60jrkb+MgY83cR1xoJHHczyi8H/osxZqmI3Az8NXCee47vARuNMf/mlj14G6d6qAHSxpiTIjIbWG6MWVCkR6OUOLo0VUqdtz0l4PKHInKN+/dUYDYwDnjNO84YE6dPwiicMgazcYR6ubXvRescX8ApNvYn7vZpOKUhdgH/KCIXACngrILvTFFioopAKXWOeX+4K4TLgcXGmOMi8gqOYBYKL/f7XeBlY8w17krklahruudeaozZbL/ZXW3sBc7HWZGcLPD6ihIb9REopUQ7TivHbIwC2lwl8AmcdoAAbwGXiMgMcMxHMc43Ctjp/n1zjmv+EvgDq9LlJ6337zbGpHEKyvXEma0osVBFoJQMxpiDwJsiskFE/jbikH8HykTkPZwZ/Wr3ffuB24BVIvIu8Lh7/M+Ba6KcxcD/BL4vIm+SW4h/F8ds9J6IbHC3Af4J+E8ishrHLHQsy/sVpdeos1hRFKXE0RWBoihKiaOKQFEUpcRRRaAoilLiqCJQFEUpcVQRKIqilDiqCBRFUUocVQSKoiglzv8DaKpxd0xVxBcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 2.083 2.157\n",
      "fractional expected GOP seats =  21.161  out of  38 seats for TX\n",
      "simpler expected GOP seats =  21.161  out of  38 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
